Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Hosseini Alhashemi, Azamalsadat
Master of Science - MSc
University of British Columbia
Northern wetlands are a major global source of methane (CH₄) emissions to the atmosphere. How carbon cycling in these peat-rich ecosystems will respond to ongoing climate change remains uncertain. Stable isotopes provide a means to trace CH₄ from different emission sources, including different wetland types; however, upscaling of emissions relies upon accurate knowledge of CH₄ source strengths and isotope composition, and delineation of wetland types and areas. This study developed a comprehensive probabilistic inventory of wetland occurrence in Manitoba, Canada using Earth observation satellite data and ancillary data characterizing vegetation structure and environmental conditions. Google Earth Engine was used to acquire and process synthetic aperture radar (SAR) and multi-spectral data from Sentinel-1 and Sentinel-2 for 2018. Satellite imagery was combined with vegetation structure information, pH and topographic derivatives in a random forest model to predict the occurrence and type of wetland. The approach yielded a high overall predictive accuracy (0.86) and strong discrimination between different wetland types. The results demonstrated a significant role for use of optical variables to model wetland distribution at regional scales when coupled with LiDAR-derived metrics and soil pH data. A literature review identified limited new stable isotope data for wetlands globally but the available data agreed well with previous assessments of δ¹³C(CH₄) values for bogs (-74.9 ± 9.8‰) and fens (-64.8 ± 4.0‰). The δ¹³C(CH₄) values were upscaled using published CH₄ flux rates and the areas of bogs and fens in Manitoba determined in this study. Manitoban bogs and fens are estimated to emit 0.8 ± 0.1 Mt CH₄ yr-¹ collectively, which is a large proportion of total flux from northern Canadian wetlands (4.1 - 6.9 Mt). Integrated δ¹³C(CH₄) values are more negative than compositions typically attributed to wetlands (-62 to -58‰) in global budgets because of the endmember compositions assigned to bogs and fens. However, the integrated values are more consistent with δ¹³C(CH₄) values reported from diel inversion and aircraft studies. Refinement of the approach will rely on new observational δ¹³C(CH₄) and source strength data for all types of boreal wetland in combination with measured δ²H(CH₄) values, which remain unavailable globally.
REFINING ESTIMATES OF THE STABLE CARBON ISOTOPE SIGNATURE ATTRIBUTED TO METHANE EMISSIONS FROM BOREAL WETLANDS by Azamalsadat Hosseini Alhashemi B.Sc. Environmental Engineering, Islamic Azad University, North Tehran Branch, 2002 M.Sc., Environmental Science, Islamic Azad University, Tehran Science and Research Branch, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE In THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2020 © Azamalsadat Hosseini Alhashemi, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Refining estimates of the stable carbon isotope signature attributed to methane emissions from boreal wetlands submitted by Azamalsadat Hosseini Alhashemi in partial fulfillment of the requirements for the degree of Master of Science. Examining Committee: Edward Hornibrook, Department of Earth, Environmental and Geographic Sciences Supervisor Mathieu Bourbonnais, Department of Earth, Environmental and Geographic Sciences Co-Supervisor Jason Pither, Departments of Biology and Earth & Environmental Science Supervisory Committee Member Melanie Jones, Department of Biology University Examiner iii Abstract Northern wetlands are a major global source of methane (CH4) emissions to the atmosphere. How carbon cycling in these peat-rich ecosystems will respond to ongoing climate change remains uncertain. Stable isotopes provide a means to trace CH4 from different emission sources, including different wetland types; however, upscaling of emissions relies upon accurate knowledge of CH4 source strengths and isotope composition, and delineation of wetland types and areas. This study developed a comprehensive probabilistic inventory of wetland occurrence in Manitoba, Canada using Earth observation satellite data and ancillary data characterizing vegetation structure and environmental conditions. Google Earth Engine was used to acquire and process synthetic aperture radar (SAR) and multi-spectral data from Sentinel-1 and Sentinel-2 for 2018. Satellite imagery was combined with vegetation structure information, pH and topographic derivatives in a random forest model to predict the occurrence and type of wetland. The approach yielded a high overall predictive accuracy (0.86) and strong discrimination between different wetland types. The results demonstrated a significant role for use of optical variables to model wetland distribution at regional scales when coupled with LiDAR-derived metrics and soil pH data. A literature review identified limited new stable isotope data for wetlands globally but the available data agreed well with previous assessments of 13C(CH4) values for bogs (-74.9  9.8‰) and fens (-64.8  4.0‰). The 13C(CH4) values were upscaled using published CH4 flux rates and the areas of bogs and fens in Manitoba determined in this study. Manitoban bogs and fens are estimated to emit 0.8 ± 0.1 Mt CH4 yr-1 collectively, which is a large proportion of total flux from northern Canadian wetlands (4.1 - 6.9 Mt). Integrated 13C(CH4) values are more negative than iv compositions typically attributed to wetlands (-62 to -58‰) in global budgets because of the endmember compositions assigned to bogs and fens. However, the integrated values are more consistent with 13C(CH4) values reported from diel inversion and aircraft studies. Refinement of the approach will rely on new observational 13C(CH4) and source strength data for all types of boreal wetland in combination with measured 2H(CH4) values, which remain unavailable globally. v Lay Summary Wetlands are the main natural source of atmospheric methane (CH4) emissions. It is uncertain how CH4 emissions from northern wetlands will be altered by climate change. The stable isotope composition (13C and 12C content) of CH4 provides a useful tool for tracing changes in emissions. Characterizing changes in CH4 emissions from natural sources is important because it provides the background on which human emissions are superimposed. The ability to estimate the amount and isotope composition of CH4 emitted from wetlands relies on being able to identity and quantify areas of different types of wetland. This study developed a new approach to estimate the areas of common wetland types in boreal regions based upon publicly available remote sensing data. It focused in particular on defining bogs and fens, which are two common types of boreal wetland that emit CH4 having distinctive carbon isotope compositions. The model approach was applied to wetlands in Manitoba as a case study showing that wetlands areas and emissions can be quantified effectively at ecozone and ecodistrict scales. vi Preface This study was conducted by Azamalsadat Hosseini Alhashemi under the supervision of Dr. Edward Hornibrook and Dr. Mathieu Bourbonnais at the University of British Columbia Okanagan campus. Compiling the data base from literature studies, writing the background, data analysis and producing final maps was completed by Azamalsadat Hosseini Alhashemi and supervised by Dr. Edward Hornibrook. Development of the probabilistic inventory of wetland occurrence in the boreal zone of Manitoba was conducted under the supervision of Dr. Mathieu Bourbonnais. This study was reviewed and examined by Supervisory Committee member Dr. Jason Pither and University Examiner Dr. Melanie Jones. All advice provided by the Supervisory Committee and all literature referenced are correctly cited. All maps in this thesis were generated using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used under license. Copyright © Esri. A version of Chapters 2 and 4 will be submitted for peer-reviewed publication. The literature review presented in Chapter 3 will be integrated into a larger multi-author overview paper and submitted for publication. This study did not involve human subjects and did not require approval from the University of British Columbia Okanagan Research Ethics Board. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Overview and Rationale .................................................................................................. 1 1.2 Classification of wetlands ............................................................................................... 5 1.2.1 Definition .................................................................................................................... 5 1.2.2 Wetland inventory ....................................................................................................... 6 1.2.3 Wetland Classification ................................................................................................ 8 1.2.3.1 Bog ...................................................................................................................... 8 1.2.3.2 Fen....................................................................................................................... 9 1.2.3.3 Swamp............................................................................................................... 10 1.2.3.4 Marsh ................................................................................................................ 10 1.2.3.5 Shallow water.................................................................................................... 11 1.2.4 Remote sensing-based wetland identification and classification .............................. 11 viii 1.3 Methane in the atmosphere ........................................................................................... 13 1.3.1 Methane sinks ........................................................................................................... 13 1.4 Use of stable isotopes.................................................................................................... 15 1.4.1 Reference standard .................................................................................................... 16 1.4.2 Stable isotope notation .............................................................................................. 17 1.5 Stable isotope measurement techniques........................................................................ 19 1.6 Stable isotope applications ............................................................................................ 22 1.6.1 Methane emission sources and processes ................................................................. 22 1.6.2 Hydrogen isotope systematics of methanogenesis from wetlands ............................ 26 1.6.3 Factors driving differences in 13C values of wetland CH4 emissions ..................... 27 1.7 Research statement........................................................................................................ 28 1.7.1 Problem and significance .......................................................................................... 28 1.7.2 Goals and objectives ................................................................................................. 30 Chapter 2: Predictive wetland modelling in Manitoba, Canada using Google Earth Engine........................................................................................................................................................32 2.1 Synopsis ........................................................................................................................ 32 2.2 Background ................................................................................................................... 32 2.3 Study area...................................................................................................................... 36 2.4 Data ............................................................................................................................... 37 2.4.1 Optical imagery data ................................................................................................. 39 2.4.2 Radar imagery data ................................................................................................... 39 2.4.3 LiDAR derived metrics ............................................................................................. 40 2.4.4 Topographic data ...................................................................................................... 41 ix 2.4.5 Wetland reference and training data ......................................................................... 42 2.4.6 pH data inventory ...................................................................................................... 44 2.5 Methods......................................................................................................................... 45 2.5.1 Random forest classification model .......................................................................... 45 2.5.2 Evaluation of the model ............................................................................................ 47 2.6 Results ........................................................................................................................... 48 2.6.1 Probabilistic wetland model ...................................................................................... 48 2.6.2 Prediction of wetland classes .................................................................................... 49 2.7 Discussion ..................................................................................................................... 59 Chapter 3: Review of recent 13C data for wetland CH4 .........................................................64 3.1 Synopsis ........................................................................................................................ 64 3.2 Background ................................................................................................................... 64 3.3 Data compilation ........................................................................................................... 67 3.4 Results ........................................................................................................................... 68 3.5 Discussion ..................................................................................................................... 74 3.5.1 Porewater CH4 .......................................................................................................... 75 3.5.2 CH4 Emissions .......................................................................................................... 77 3.5.3 Assigning 13C Values to Wetland CH4 Emissions .................................................. 79 Chapter 4: Upscaling of CH4 flux and 13C values from wetlands in Manitoba....................81 4.1 Synopsis ........................................................................................................................ 81 4.2 Background ................................................................................................................... 81 4.3 Methods......................................................................................................................... 83 4.3.1 Estimation of the area of wetland types .................................................................... 83 x 4.3.2 Estimation of CH4 flux .............................................................................................. 84 4.3.3 Isotope-weighting CH4 emissions and sensitivity analysis ....................................... 85 4.4 Results ........................................................................................................................... 87 4.4.1 Methane emission flux from bog and fen ................................................................. 87 4.4.2 Isotope-weighted CH4 emissions .............................................................................. 88 4.4.3 Sensitivity of 13C values to CH4 flux rates.............................................................. 92 4.5 Discussion ..................................................................................................................... 96 Chapter 5: Conclusions and future directions ..........................................................................99 5.1 Conclusions ................................................................................................................... 99 5.2 Future directions ......................................................................................................... 102 Bibliography ...............................................................................................................................103 Appendices ..................................................................................................................................134 Appendix A ............................................................................................................................. 134 xi List of Tables Table 1. Wetland extent in Canada circa 2000. .............................................................................. 7 Table 2. Wetland classification by the Canadian Wetland Classification System ......................... 8 Table 3. Relative abundance of three most related stable isotopes in this research ..................... 17 Table 4. Summary of data used in this study. ............................................................................... 38 Table 5. Summary statistics of the wetland proportions in the original data. ............................... 43 Table 6. Confusion matrix based on the number of pixels for the RF classifier algorithm with the user accuracy, producer accuracy, errors of commission, and omission (in %). .......................... 48 Table 7. Summary of global wetland studies since 2009 that have reported stable isotope data for CH4. ............................................................................................................................................... 70 Table 8. 13C values of porewater CH4 from different depths in northern peatlands reported since 2009............................................................................................................................................... 72 Table 9. 13C values of wetland CH4 emissions sampled from diel inversions, flux chambers and aircraft reported since 2009 .......................................................................................................... 73 Table 10. The area of wetland types from maps 5 to 9 in each ecozone of Manitoba. ................. 84 Table 11. Compilation of CH4 flux data for bogs and fens in Manitoba (Webster et al., 2018). . 86 xii List of Figures Figure 1. Canadian Wetland Classification System ........................................................................ 6 Figure 2. The study area is the province of Manitoba, Canada. Ecozones of Manitoba are shown in the inset image. ......................................................................................................................... 37 Figure 3. Wetland inventory in southern part of Manitoba, Canada. The gray color areas in Manitoba map, top right corner, show the available wetland inventory and identified red square shows the extent of the provided example. ................................................................................... 44 Figure 4. Flowchart of the methodology used to produce the probabilistic wetland maps for Manitoba, Canada. ........................................................................................................................ 46 Figure 5. Probability map of bog in Manitoba, Canada ................................................................ 50 Figure 6. Probability map of fen in Manitoba, Canada................................................................. 51 Figure 7. Probability map of marsh in Manitoba, Canada ............................................................ 52 Figure 8. Probability map of swamp in Manitoba, Canada. ......................................................... 53 Figure 9. Probability map of shallow/open water in Manitoba, Canada. ..................................... 54 Figure 10. Probability of wetland classes in Manitoba using Random Forest Model. ................. 55 Figure 11. Variable importance for wetland probability model in Manitoba, Canada. The variables are Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Near Infrared Band (B8), Normalized Polarization (NPol), B2 (blue), Canopy height (FELEV), standard deviation of canopy height (FIRST2M), B3 (green), VV Standard Deviation (VVsd), topographic wetness index (TWI), coefficient of variation of canopy height (FIRSTAM), Topographic Position Index (TPI), Total Aboveground Biomass (TBIOM), Vertical Polarization (VV), B4 (red). .......................................................................................................... 57 xiii Figure 12. Partial dependence plots, which show the predicted wetland class probability based on a single covariate while holding all other covariates at their mean value, for each of the top eight covariates in the model. The variables are Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Near Infrared Band (B8), Normalized Polarization (NPol), B2 (Blue), pH, Canopy height (FELEV) and standard deviation of canopy height (FIRST2M). ....................................................................................................................... 58 Figure 13. Global distribution of wetland sites for which stable isotope data for CH4 have been reported since 2009. The numbers correspond to labels in Table 7, which provides details about wetland location, type, dominant vegetation and publication source. .......................................... 69 Figure 14. Average flux of CH4 from bogs (a) and fens (b) of different ecozones, Manitoba. .... 89 Figure 15. Average flux from bogs (a) and fen (b) sites of different ecodistricts, Manitoba, Canada........................................................................................................................................... 90 Figure 16. Wetland 13C(CH4) source signature map (‰) for ecozones (a) and ecodistricts (b) of Manitoba ....................................................................................................................................... 91 Figure 17. Change in 13C values of CH4 flux from bog areas in the four main ecozones of Manitoba as a function of ten different scenarios for CH4 flux (horizontal axis) as well as (a) lower (-95.3 ‰), (b) mean (-74.9 ‰) and (c) upper (-55.2 ‰) limits of 13C (‰) from Hornibrook 2009. Each scenario is parameterized using Eq. 17, which is based upon areal extent (m2), CH4 flux (g m-2 season-1) and three values for 13C (‰) of bog emissions in each ecozone. In the bog scenarios, the CH4 flux for fens is held constant at 82 mg m-2 d-1 while the CH4 emissions from bogs is changed stepwise from 10 to 100 mg m-2 d-1. For example, in scenario 3 with mean value of 13C in the Taiga Shield the 13C(CH4) (‰) was calculated as: [(areal extent xiv of bog in ecozone × 30 mg m-2 d-1 CH4 flux × (-74.9‰)) + (areal extent of fen in ecozone × 82 mg m-2 d-1 CH4 flux) × (-68.4‰)] / [(areal extent of bog in ecozone × 30 mg m-2 d-1 CH4 flux) + (areal extent of fen in ecozone × 82 mg m-2 d-1 CH4 flux)]. Same method was used for the lower and upper limits of 13C (‰) for bog areas. The average number of growing days for each ecozone was used in each calculation. .......................................................................................... 94 Figure 18. Change in 13C values of CH4 flux from fen areas in the four main ecozones of Manitoba as a function of ten different scenarios for CH4 flux (horizontal axis) as well as (a) lower (-72.6 ‰), (b) mean (-64.8 ‰) and (c) upper (-55.6 ‰) limits of 13C (‰) from Hornibrook (2009). Each scenario is parameterized using Eq. 17, which is based upon areal extent (m2), CH4 flux (g m-2 season-1) and 13C (‰) of fen and bog emissions in each ecozone. In the fen scenarios, CH4 flux from bogs is held constant at 40 mg m-2 d-2 and the CH4 flux strength from fens changed stepwise from 50 to 140 mg m-2 d-1. For example, in fen scenario 3 with mean value of 13C in the Taiga Shield the 13C(CH4) (‰) was calculated as: [(areal extent of fen in ecozone × 70 mg m-2 d-1 CH4 flux × (-64.8‰)) + (areal extent of bog in ecozone × 40 mg m-2 d-1 CH4 flux) × (-74.9‰)] / [(areal extent of fen in ecozone × 70 mg m-2 d-1 CH4 flux) + (areal extent of bog in ecozone × 40 mg m-2 d-1 CH4 flux)]. Same method was used for the lower and upper limits of 13C (‰) for fen areas. The average number of growing days for each ecozone was used in each calculation. .......................................................................................... 96 xv List of Abbreviations ALOS: Advanced Land Observation Satellite BRT: Boosted Regression Tress CART: Classification and Regression Trees CEC: Cation Exchange Capacity CF-IRMS: Continuous Flow- Isotope Ratio Mass Spectrometry CWCS: Canadian Wetland Classification System CWI: Canadian Wetland Inventory DEMs: Digital Elevation Models EPA: Environmental Protection Agency GEE: Google Earth Engine GWP: Global Warming Potential IAEA: International Atomic Energy Agency KIEs: Kinetic Isotope Effects LiDAR: Light Detection And Ranging MHHC: Manitoba Habitat Heritage Corporation NI: Near Infrared NIST: National Institute of Standards and Technology NDVI: Normalized Difference Vegetation Index NDWI: Normalized Difference Water Index IRMS: Isotope Ratio Mass Spectrometry NWI: National Wetland Inventory xvi PDP: Partial Dependence Plots Pol: Normalized Polarization R: Isotope Ratio RF: Random Forest RS: Remote Sensing SAR: Synthetic Aperture Radar TDLAS: Tunable Diode Laser Absorption Spectrometer TPI: Topographic Position Index TWI: Topographic Wetness Index VPDB: Vienna Belemnite from the PeeDee formation VSMOW: Vienna Standard Mean Ocean Water VV: Vertical Polarization VH: Vertical-Horizontal Polarization VVsd: Vertical Polarization Standard Deviation USFWS: United States Fish and Wildlife Service UTM: Universal Transverse Mercator xvii Acknowledgements I would like to express my sincere gratefulness to my supervisor, Dr. Edward Hornibrook, for his support and encouragement throughout this research. I will be forever grateful for the opportunity of being his student and work under his supervision. I would like to appreciate my co-supervisor Dr. Mathieu Bourbonnais for all his technical and invaluable help and guidance for maps productions and machine learning approach, without his help I would be lost. I would like to thank the rest of my committee member Dr. Jason Pither, and external examiner Dr. Melanie Jones for providing valuable perspectives and advice. I am thankful to the amazing administrative and personnel of Earth, Environmental Science, and Geography Department for their support and help, especially, Janet, Stuart and Christina who helped me so much during my study. I am very grateful for my friends, Leah Rensel, Sultana Abdolmajid, and Jennifer Fulks who supported me for English editing and working by GIS. And of course, My good friends Marriani and Grace, thanks a lot for your supports. On a more personal level, I would like to express my deep appreciation to my husband, Mohammad, for his continuous and unconditional love, patient and support. And last, but not least, my dear Mother who always supports me, no matter how far we are. Thank you all for supporting me either scientific or spiritual. This accomplishment would not have been possible without all your supports. xviii Dedication For: My lovely husband: Mohammad And my loving children: Armita, and Artin 1 Chapter 1: Introduction 1.1 Overview and Rationale Methane plays a key role in atmospheric photochemistry since it can control the formation of hydroxyl (OH*), and ozone (O3), two significant elements for the breakdown of most gases in the atmosphere emitting through natural and anthropogenic activities (McConnell et al., 1971; Crutzen, 1973). It is the third strongest absorber of infrared radiation in the atmosphere after water vapor and carbon dioxide (CO2) (Heimann, 2010), with the global warming potential (GWP) of 25 over 100 years (Lelieveld et al., 1998). The concentration of CH4 has shown increases with time since onset of industrialization in the 17th and 18th centuries (Khalil & Rasmussen, 1987; Chappellaz et al., 1990), particularly in the last 150 years because of human population growth (Rasmussen & Khalil, 1984). The combination of atmospheric CH4 lifetime (remaining time in the atmosphere; 8 to 12 yr) (Khalil & Rasmussen, 1987) and its increasing rate, restrict total global CH4 emissions to between 500 and 600 Tg CH4 yr-1 (Dlugokencky et al., 2011). However, there are large variations in the growth rate of atmospheric CH4 year to year which leads to a poor characterization of the global cycle and budget of CH4 (Langenfelds et al., 2002; Dentener et al., 2003). Amongst the natural emission sources, wetlands occupying 2% to 6% of Earth land surface (Buringh, 1984; Aselmann & Crutzen, 1989) , emit ~30% (150 Tg CH4 yr-1) of the total CH4 flux (Khalil & Rasmussen, 1987; Bousquet et al., 2006) to the Earth’s atmosphere. There is much uncertainty about how natural systems such as wetlands, will respond to global changes in temperature and moisture (Moore et al., 1998). There are three main factors determining CH4 emission from wetlands including water table depth (Moore & Dalva, 1993), temperature and substrate availability, of which temperature has been found to be the dominant factor by increasing 2 the rates of microbial degradation (Reiners, 1968; Christensen et al., 2003). Wet conditions typically promote anaerobic processes, which are required for methanogenesis (Moore & Knowles, 1990; MacDonald et al., 1998). Dry conditions lower the water table resulting in greater methane oxidation and less methane production (Keller, 1986; Crill, 1991). Wetland response to unusual weather has contributed to interannual variability in methane increases in the atmosphere. For example in 2007 an increase in emissions of 2–3 Tg CH4 (Bergamaschi et al., 2013; Bruhwiler et al., 2014) resulted to unusually high temperature and southerly winds in the Arctic region (Comiso et al., 2008; Kay et al., 2008). The majority of northern wetlands (north of 45 ° N) cover northern Canada and Siberia (Bartlett & Harriss, 1993) with cool temperate, boreal, and subarctic forest zone (Roulet et al., 1992). Wetlands in Canada occupy ~13% of its terrestrial area (Wulder et al., 2018) and its north is rich in wetlands with a diverse set of climatic, physiographic and, ecological conditions ( Roulet et al., 1992; Halsey et al., 1997). In the northern high latitudes, peatlands contain up to 25% of the world’s soil carbon (C) (Roulet et al., 2007), for which higher soil decomposition, boosted thawing of permafrost and enhanced wildfire have been accounted as the main factors for CH4 and CO2 emissions (Worthy et al., 2000; Zhang et al., 2013). Wetlands in northern Canada are commonly either rainfed ombrotrophic (a type of peatland which is hydrologically isolated and dependents on precipitation for water and nutrient supply) bogs or minerotrophic (a peatland type with high dissolved mineral content fed by groundwater recharge (main resource) and runoff) to some extent, receiving input of groundwater that raises pH and increases the supply of base cations to the wetland (Moore & Dalva, 1993; Moore et al., 1998). Different types of wetland including bog, fen, marsh, swamp, and shallow/open water occupy 10%, 18%, 33%, 23% and 15% of the wetland areas of Canada, respectively (Amani et al., 2019). 3 Methane is produced by different metabolic pathways in bogs and fens most likely due to the influence of environmental conditions (typically the pH of water in bogs ranges from 4.0 to 4.8 and in fens from 5.5 to 7.0; Glooschenko et al., 1993)) on the types of archaea and bacteria (Chanton et al., 2005), which impacts the substrates that are utilized predominantly in bogs (CO2/H2) and fens (acetic acid) (Whiticar, 1999). pH is a significant factor controlling the activity of microbial communities through its influence on the availability and chemical form of substrates, which can affect the growth and metabolic activity of cells (Veloso et al., 2017a). Differences in metabolic (substrate-based) methanogenic pathways in bogs and fens yield CH4 that has different stable isotope compositions (Chanton, 2005; Hornibrook, 2009). The pathway impacts are best characterized for stable isotope of CH4 (Brownlow et al., 2017; Fisher et al., 2017), which provides a valuable means to understand the specific chemical, physical and biological aspects of global CH4 cycle (Hornibrook & Sugimoto, 2005). There is some evidence that pathway differences also impact the deuterium isotope composition of CH4 but accurately characterizing those differences has not been achieved to date, in part, because other factors such as the stable hydrogen isotope composition of ambient water also impacts the deuterium content of CH4 (Burke et al., 1988; Brosius et al., 2012). A lack of stable isotope data and accurate maps of the distribution and areas of bogs and fens in the boreal zone has hampered the ability to use stable carbon isotope values of CH4 to better constrain CH4 emissions from northern wetlands and to assess the role of weather-response in wetlands (Hopcroft et al., 2017; Zhang et al., 2018) in the interannual variability of growth rate of atmospheric CH4. The most recent assessment of carbon isotope data from wetlands was conducted over a decade ago (Hornibrook 2009). The present study will review the literature to determine the extent to which new data exists and then reexamine the δ13C values from the 2009 compilation. 4 The project will rely upon archival data to ground truth remote sensing data for the purpose of better defining the areal extent of bogs and fens. The work will be conducted for the boreal zone in Manitoba where historical data for both land cover and greenhouse gases are available. In addition, Webster et al. (2018) have compiled the results for CO2 (net ecosystem exchange) and CH4 emission from bogs and fens across Canada and a new peatlands map distinguishing bog from fen which reported the flux emission of CH4 per growing season in each single ecozone of Canada (Webster et al., 2018). Their compiled data of CH4 flux in Manitoba province has been used in the present study to predict the emission of CH4 from Manitoba’s bogs and fens based on the newly generated wetland maps in the present study. Hornibrook (2009) has compiled the composition of δ13C(CH4) produced and emitted from northern peatlands based on the different wetland types (ombrotrophic bogs vs. minerotrophic fens). In order to delineate fens and bogs in Manitoba, a series of data and model predictions (Wulder et al., 2012; Hird et al., 2017; Matasci et al., 2018), as well as ground truth data in the southern part of this province (Government of Manitoba, 2017) have been used that are presented in Chapter 2. One goal of the study is to build on the finding of Ganesan et al. (2018), which showed that distinguishing CH4 emissions from fens and bogs isotopically is important in global budgets. The aim is to evaluate further whether currently used stable C isotope values from global mass balance budgets (e.g. -67‰ (Ganesan et al., 2018; Fisher et al., 2017); -61‰ (Thompson et al., 2016)) are an accurate representation of CH4 emissions from boreal wetlands or whether the emission δ13C signature should be revised. The sensitivity of the net δ13C(CH4) value from Manitoba and ecozones and ecodistricts will be examined in the context of variability in emission strength and its isotope composition. Area of bogs and fens in each ecozone and ecodistrict as well as the duration of growing seasons are the most important variables affecting the flux and δ13C of CH4. 5 1.2 Classification of wetlands 1.2.1 Definition Wetland areas have important valuable ecological functions including hydrologic flux and storage; biological productivity; biogeochemical cycling and storage; decomposition; and providing habitat for community or wildlife (Lyon & Lyon, 2011; Mitsch & Gosselink, 2000). Additional values of wetlands are floodwater storage, sediment control, wastewater treatment, recreation, preservation of fauna and flora, food production, education and research (Richardson, 1994). The Ramsar Convention gives one of the most comprehensive and commonly used definitions of wetland (Davis 1997): “Areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed 6m.” Although this definition is widely accepted internationally, most countries have their own definition of wetland developed for various means including science, management, and governmental levels. Since the present study is focused on boreal wetlands in Canada, it will use the definition of wetland from the National Wetland Working Group (National Wetlands Working Group, 1997) which is “an area that is saturated with water long enough to promote wetland or aquatic processes as indicated by poorly drained soils, hydrophytic vegetation and various kinds of biological activities that are adapted to a wet environment.” According to the National Wetland Working Group (1997) two main categories of wetland areas are organic and mineral wetlands (Figure 1). Peatland is used to refer to wetlands that contain 40 cm or more of naturally accumulated organic peat soil. Mineral wetlands are found in mineral soil areas related to shallow water along with naturally fluctuating water levels (National Wetlands 6 Working Group, 1997). There is little or no accumulation of organic matter in the shallow / open water because of hydrologic, morphologic, vegetation cover or climate factors (National Wetlands Working Group, 1997). 1.2.2 Wetland inventory Wetland inventories around the world have been identified with different methods, goals and variable extent coverage (Finlayson et al., 1999; Prigent et al., 2001; Ringeval et al., 2010; Melton et al., 2013). Wetland extent across the United States was mapped through manual interpretation of aerial images and the inclusion of soil data, topographic maps and information gathered from field observations (Kuzila et al., 1991; Tiner, 1990). About 13% of Canada’s land (1.29 million km2) is covered by wetlands which has made Canada one of the greatest stewards globally of these important ecosystems (Table 1). Several attempts have been made in Canada to prepare provincial-Wetlands Mineral Wetlands Organic Wetlands (Peatlands) Shallow open water Marshes Swamps Marshes Swamps Bogs Fens Figure 1. Canadian Wetland Classification System Wetlands are classified into two wide groups including peatlands and mineral wetlands Source: (National Wetlands Working Group, 1997) 7 based inventories, which were not addressed until 2002 by the foundation of Canadian Wetland Inventory (CWI) partnership between Ducks Unlimited Canada, the North American Wetlands Conservation Council, and the Canadian Space Agency (Mahdavi et al., 2018). The development of the wetland classification system was the starting point of creating wetland inventories, which are discussed in the next section. Table 1. Wetland extent in Canada circa 2000. Label Ecozone Wetland area (km2) Proportion of ecozone that is wetland (%) L01 Arctic Cordillera 1059 0.45 L02 Northern Arctic 48 877 3.4 L03 Southern Arctic 52 472 5.6 L04 Taiga Plains 138 737 25 L05 Taiga Shield 145 873 11 L06 Boreal Shield 320 778 16.9 L07 Atlantic Maritime 6920 6.3 L08 Mixedwood Plains 12 857 11.1 L09 Boreal Plains 235 938 30.3 L10 Prairies 14 269 3.1 L11 Montane Cordillera 8152 1.86 L12 Pacific Maritime 2587 1.2 L13 Boreal Cordillera 7121 1.28 L14 Taiga Cordillera 2581 1.12 L15 Hudson Plains 276 230 78.8 L16 Tundra Cordillera 188 0.65 L17 Atlantic Highlands 3536 3.8 L18 Semi-Arid Plateaux 460 0.81 Total 1 290 187 12.9 Note: The sum of the wetland area of the ecozones does not equal the overall total due to the generalization of boundaries at the national scale. (Source: Canadian Wildlife Service, 2016) Available on the Canadian Environmental Sustainability Indicators (www.ec.gc.ca/indicateurs-indicators/default.asp). 8 1.2.3 Wetland Classification In the Canadian wetland classification system, the lower levels are very specific and detailed criteria, and the higher ones are more descriptive and definitive criteria (Zoltai & Vitt, 1995). There are three hierarchical levels in the Canadian wetland classification system including “Class”, “Form” and “Type”. The classes represent the general source of wetland ecosystem. The basis of water type, surface morphology and pattern, and geomorphology of subsurface mineral soil are differentiated in the forms while types are recognized based on the vegetation feature (Zoltai & Pollett, 1983). Accordingly, the Canadian wetland classification system has identified five major wetland classes, including bogs, fens, swamps, marshes, and shallow water (Table 2), which are based on soil type, water table depth, water flow, nutrient availability and vegetation communities (National Wetlands Working Group, 1997). Table 2. Wetland classification by the Canadian Wetland Classification System Class Description Bog Ombrotrophic peatland dominated by sphagnum moss species Fen Minerotrophic peatland dominated by graminoid species and brown mosses Swamp Peatland or mineral wetland dominated by woody vegetation Marsh Minerotrophic wetland with periodic standing water or slow-moving water, dominated by graminoids, shrubs, forbs, and emergent plants Shallow Water Minerotrophic wetland where water is up to 2 m deep for most of the year and where there is less than 25% of emergent plants or woody plants 1.2.3.1 Bog A bog is a Sphagnum-dominated, peat-covered area with a high water table (Zoltai & Vitt, 1995). Precipitation is the main source of water and nutrient in these wetlands (ombrogenous wetlands) (Smith et al., 2007). Bogs are acidic mainly due to formation of humic acid during 9 decomposition processes (Hemond, 1980) with the pH between 4.0 and 4.8 (Glooschenko et al., 1993). The bog is low in nutrients because its surface is either higher or equal to the surrounding wetlands and almost unaffected by the nutrient-rich ground waters from the close mineral soils. Also, peat accumulation in bog is from Sphagnum or forest peat developed in an ombrotrophic ecosystem (Canadian Department of Agriculture, 1978). Bogs can be both treed or treeless landforms characterized as Sphagnum peat moss accumulations and ericaceous shrubs. The main materials that are found in the peat are decomposed of Sphagnum and woody remains of shrubs (Zoltai and Pollett 1983; National Wetlands Working Group 1997). 1.2.3.2 Fen Fen is a peatland with high and fluctuating water tables. The surface of fen typically is at the level of the surrounding terrain (National Wetlands Working Group 1997). Its water is largely nutrient-rich due to accumulations of peat formed from decomposed graminoids at surface layers (Glooschenko et al., 1993). Moreover, the chemistry of the surrounding mineral soil deposits affect the fens (Zoltai & Vitt, 1995). The vegetation is dominated by graminoid, and in more wet areas, grasses and reed can be found. While the presence of mosses is widespread, Sphagnum is commonly absent or dependent on local conditions. Low to medium height shrubs are important in fens and enabled by lower water table levels. A thin cover of trees is common in the driest fen sites (Zoltai and Pollett 1983). The fen sites with very low dissolved minerals dominated by Sphagnum mosses and ericaceous shrub are called ‘poor fen’. Moderate-rich fen is dominated by sedges and brown mosses with more dissolved minerals in the water, and a pH between 5.5 and 7.0, which commonly is influenced by water from nearby uplands (Zoltai & Vitt, 1995). Extremely 10 rich fens are covered dominantly by sedges and brown mosses, as well as shrubs and Larix laricina and a pH >7.0 (Zoltai and Pollett 1983; National Wetlands Working Group 1997). 1.2.3.3 Swamp Swamp is a peatland or a mineral wetland where the water table is at or near the surface soil and its water can be standing or slowly flowing (Zoltai and Pollett 1983). Swamps have nutrient rich water due to the movement of water from the margins or other mineral sources (Glooschenko et al., 1993). This type of wetland is close to the treed bog where growth of trees prevents water movement, and is not as wet as marshes, fens and open bogs. The highly variable range of nutrient regimes in swamps is the main cause of variable pH between 7.0 and 4.5 or lower, especially in areas of acidic ground water (Zoltai & Vitt, 1995). Swamps vegetation cover is dominated by coniferous or deciduous trees, tall shrub, herbs and mosses (National Wetlands Working Group 1997; Zoltai and Pollett 1983). 1.2.3.4 Marsh Marshes are mineral wetlands or peatlands with shallow standing or slowly moving waters (Zoltai et al., 1975). This type of wetland has relatively extreme water movement which can fluctuate daily, seasonally or annually from tides, flooding or groundwater changes (National Wetlands Working Group 1997). Marshes can be present in different geographical areas such as low-level areas next to water bodies (e.g., rivers, lakes) or other places on the land surface where groundwater may discharge (Zoltai & Vitt, 1995). The substrate of marshes generally contains organic soils and thus, it is generally a eutrophic wetland. The significant decomposition rates of vegetation and high productivity of vascular plants during the growing season lead to the high 11 nutrient levels but limited peat accumulation (Zoltai and Pollett 1983). The main vegetation cover of marshes consists of emergent non-woody macrophytes (rushes, reeds, reed grasses, sedges), shrubs and other plantlike species broad-leaved emergent macrophytes and in the areas near open waters, floating aquatic and submerged species (Glooschenko et al., 1993). 1.2.3.5 Shallow water Shallow water wetlands are transitional to deep, open water and have vegetation that tolerates permanent flooding (Vitt & Slack, 1975). The properties of water such as dissolved minerals, acid-base balance and nutrient levels depend on hydrology, subsurface geological materials, nutrient levels and vegetation cover (Zoltai & Vitt, 1995). The differentiation of shallow and deep waters is based on the upper 2-meter limit, although these depths can be affected during periods of abnormal flooding or droughts (Glooschenko et al., 1993). The bays and borders of deep lake zones may be classified as shallow water wetlands (Zoltai and Pollett 1983; National Wetlands Working Group 1997). 1.2.4 Remote sensing-based wetland identification and classification Remote sensing (RS) technology along with the advent and development of geospatial data has led to more exact maps identifying different land covers (Tiner et al., 2015). Some approaches include advanced Earth observation satellites with high spatial resolution (10-100 m) (Gorelick et al., 2017) and open access strategies (i.e., Sentinel Earth Observing Mission) (Belward & Skøien, 2015), long-term and detailed image accessibility (i.e., the Landsat mission) (Hermosilla et al., 2015), improved computer processing techniques, and increasing development and usage of machine learning (Hird et al., 2017; Wulder et al., 2018). 12 Remote sensing has proven to be successful in monitoring wetlands in a less costly and time-consuming way for large geographic areas than field studies (Kaplan & Avdan, 2017); however, identifying wetland areas remotely is a challenging task due to: a) variability in wetland coverage from open water (vegetation free) to fully vegetated surfaces through growing seasons or across years, and b) changes in water level/depths resulting from snowmelt, rainfall and groundwater) (Gallant, 2015). The major advantage of remote sensing is providing a continuous and repeatable means of monitoring ecosystems, while also allowing the monitoring of large areas that would otherwise be difficult to access comprehensively using ground-based methods (Klemas, 2015). Also the digital format of remote sensing products can be suitably integrated and combined with other information types in a Geographical Information System (GIS) (Ozesmi and Bauer 2002; Tiner et al., 2015). Different methods such as aerial photo interpretation (Shima et al., 1976; Tiner, 1990) and unsupervised/supervised classification (Prigent & Aires, 2001; Ozesmi & Bauer, 2002; Sawaya et al., 2003), have been used for the classification of wetlands based on remote sensing data products. However, there are technical difficulties, which make classification of wetlands a challenging task including: • Water presence is a typical element in wetland areas where it can be shallow and cover just the roots of the plant. In addition, it can be present and absent seasonally or due to human activities. All of these limitations lead to difficulties identifying wetlands (Gallant, 2015). • Different wetland types may be combined inadvertently as a single class using remote sensing because of similar ecological characteristics. Consequently, different wetland types can have similar spectral and/or backscatter features in optical and synthetic aperture radar (SAR) data (Henderson & Lewis, 2008). Problems defining wetland borders delineating the 13 transition area between wetland and surrounding land cover types is another issue causing difficulty in detection of wetland extent (Dronova, 2015). 1.3 Methane in the atmosphere Methane is one of the most important greenhouse gases with a global warming potential (GWP) of 25 over 100 years (Boucher et al., 2009; Denman et al., 2007). The GWP with the time perspective of 100 years is used in the Kyoto Protocol (IPCC, 2014). Methane has an important effect in the Earth’s radiation budget and atmospheric chemistry, not only in the troposphere but also in the stratosphere (IPCC 1995). The long-term global trend for atmospheric CH4 reveal the relative steadiness for hundreds of thousands of years, but modern measurement tools facilitating CH4 concentration monitoring are able to show changes in CH4 levels during the past five decades (Frankenberg et al., 2005; Dlugokencky et al., 2011; Nisbet et al., 2014). These techniques show a sharp increase in methane concentration of approximately 8 ppb/yr since 2006 (Worden et al., 2017), leading to a total atmospheric burden of CH4 that exceed pre-industrial levels by ~150%, (Cubasch et al., 2013). The concentration of atmospheric CH4 has increased from 1272 ppb in 1965 to 1870 ppb in 2019, though recent years (2015-2019) showed a sharp increase in the average of atmospheric CH4 for reasons that are not fully understood (Dlugokencky, 2019). 1.3.1 Methane sinks In the troposphere, CH4 reacts with the OH* radical producing CO, water, and CO2, consequently CH4 can affect the concentrations of ozone, hydroxyl radicals, and CO in the troposphere, and of ozone and chlorine in the stratosphere (IPCC 1995, 2014; Law and Pyle 1993; Donner and Ramanathan 1980). The primary natural sinks of CH4 include soil functions (e.g., 14 methane-oxidizing microorganisms; Topp & Pattey, 1997), destruction in the stratosphere, and oxidation in the troposphere by hydroxyl which involve 5%, 7%, and 88% of the total sink, respectively (Boucher et al., 2009). Two main final products for the atmospheric CH4 are water vapor and carbon dioxide (Forster et al., 2007). The main reactant destroying CH4 is the gas phase hydroxyl radical, OH*, which consumes the oxidized forms (CO, CO2, H2O, H2, CH2O) of methane (Cicerone & Oremland, 1988). Subsequently, OH* can lead to the generation of ozone which can add the effects of CH4 in the atmosphere (Reay et al., 2010). OH* which is a key parameter in tropospheric chemistry, originates from ozone (O3), ultraviolet (UV) radiation and water vapor (Eq. 1 and 3) (Cicerone & Oremland, 1988; Lu & Khalil, 1993). O3 + hv → O(1D) + O2 (Eq. 1) The main components of an excited oxygen atom, O(1D), are deactivated by collision with nitrogen (N2) or oxygen (O2) molecules in troposphere (Eq. 2) and the remainder O(1D) reacts with water vapor to produce hydroxyl radical, OH* (Eq. 3) whose reaction with atmospheric CH4 is the main sink of methane (Eq. 4) (Cicerone & Oremland, 1988; Lu & Khalil, 1993). O(1D) + N/O → O + N2/O2 (Eq. 2) O(1D) + H2O → 2OH* (Eq. 3) CH4 + OH* → H2O + CH3 (Eq. 4) In the sequence of these reactions eventually formaldehyde, then CO, and ultimately CO2 and H2O, will be produced consuming additional OH* radicals Ravishankara, 1988; Carslaw et al., 15 2000). When CH4 reaches the stratosphere, it will be destroyed by OH* or by Cl* radicals. Cl* radicals (highly reactive to ozone) are trapped in the stratosphere under a form (HCl) less reactive toward ozone (Bréas et al., 2001). Methane oxidation can produces CO, as well as either an increase or decrease of O3 in the atmosphere, while nitrogen oxide (NO) abundance affects the CH4 oxidation chemistry as well (Thompson & Cicerone, 1986). Methane oxidation produces ozone molecules when there are adequate amounts of NO, and consumes O3 when NOx is low (Wahlen, 1993). The main pathways that control the levels of OH are oxidation of methane, CO, and non-methane hydrocarbons which could deplete atmospheric OH (Wahlen, 1993). Another natural sink is soil, where methane oxidation is mediated by methanotrophic bacteria (Bréas et al., 2001). Anaerobic methanogenesis is the main contributor of CH4 production in soils (Watanabe et al., 2007), or in flooded soils where CH4 production takes place under conditions of low redox potential (Dalal et al., 2008). Northern-latitude soils play a substantial role in the global budget of CH4 since one-third of the global organic C pool is located there (Post et al., 1982). The soils present in high latitudes, are water saturated during summer thus, there is a large CH4 production due to anaerobic metabolism. Furthermore, the effects of climate change on Arctic wetland areas will be stronger than the global average and will increase with time (Wuebbles & Hayhoe, 2002). 1.4 Use of stable isotopes Versions of elements that have different mass numbers due to a different number of neutrons are known as isotopes. The isotope ratio (R) represents the ratio of the heavy (rare) isotope to the light (abundant) isotope (e.g., 2H/1H, 13C/12C, 18O/16O; Criss & Farquhar, 2008). The different isotopes of an element have the identical number of protons (which defines the element) and 16 electrons so they are largely chemically equivalent; however, the difference in mass results in minor but important differences in their physical behavior and reaction kinetics ( Sulzman, 2007; Criss & Farquhar, 2008). These differences in behavior during processes in the Earth system lead to partitioning of isotopes, or compounds bearing the different isotopes, which makes stable isotopes a useful resource for tracing and identifying sources of compounds, characterizing processes and constraining reaction rates, and estimating inputs in mass balance models (Lowe et al., 1991; Sulzman, 2007). Consequently, stable isotopes have been employed extensively in the study of the global methane cycle, for example, to identify biological methane production pathways (Whiticar et al., 1986), to distinguish biogenic and thermogenic methane (Whiticar, 1999), to estimate the proportion of methane consumed by methanotrophic bacteria (Bogner et al., 1995, 1996, 2000), and for tracing methane emissions sources in global mass balance budgets (Schaefer et al., 2016). This section provides an overview of stable isotope systematics and their use in methane cycle research, which will serve as an introduction for their application in tracing methane emissions from natural bogs and fens in Chapter 3. 1.4.1 Reference standard Stable and sufficiently analogous substances used to calibrate stable isotope ratios during measurement are called reference materials (Vaughnl & Zealand, 2004). The International Atomic Energy Agency (IAEA) and the National Institute of Standards and Technology (NIST) are the primary sources for international standards for stable isotope analysis, which enables accurate global comparison of stable isotope ratios in natural and synthetic materials. A summary of the most common isotope standards relevant to methane cycle research is provided in Table 3. The 17 reference standards enable calibration of C, H, and O stable isotope ratios in key products and reactants, including CH4, CO2, H2O, H2, CH3COOH and range of high and low molecular weight organic compounds. Table 3. Relative abundance of three most related stable isotopes in this research Element Isotope Abundance (%) Relative mass Difference (%) International Standard Absolute abundance of the standard (Rstandard) Hydrogen 1H 99.985 100 Vienna Standard Mean Ocean Water (VSMOWa) 2H:1H = 1.5575×10-4 2H 0.0155 Carbon 12C 98.892 8.3 Vienna Pee Dee Belemnite (VPDBb) 13C:12C = 1.1237×10-2 13C 1.108 Oxygen 16O 99.759 12.5 (18O:16O) VSMOW in water, generally VPDB in CO2 or carbonate VSMOW = 2.0052×10-3 VPDB = 2.067210-3 (18O:16O) 17O 0.037 18O 0.204 a The original standard was 'Standard Mean Ocean Water' (SMOW) which is no longer available; however, 'Vienna'-SMOW (V-SMOW) is available from the IAEA; b the original standard was a Belemnite from the PeeDee formation (PDB) which is no longer available; however, 'Vienna'-PDB (V-PDB) is available from the IAEA. 1.4.2 Stable isotope notation The isotopic composition of a sample is expressed using the standard delta () notation. This notation was introduced by McKinney et al. (1950) and is used to report the ratio of heavy (rare) to light (common) isotopes of an element measured in a sample of interest relative to a defined international stable isotope standard. Stable isotope ratios rather than absolute abundances are used, because isotope ratios can be determined much more accurately by mass spectrometry. The -value of a sample expressed in permil (‰) is the relative difference between the isotope ratio (e.g, 13C/12C) of the sample (Rsample) and the reference standard (Rstandard): 13C(CH4) = ((Rspl/Rstd) - 1) Eq. 5 18 In the case of calculating 13C(CH4), R is the ratio of 13C/12C in CH4 and the standard Vienna PeeDee Belemnite (Craig, 1957; Criss & Farquhar, 2008; Sharp, 2017). Older representations of the equation used to calculate -values includes a ‘x 1000’ factor; however, the current International Union of Pure and Applied Chemistry (IUPAC) guidelines recommend use of equation 5 because the unit of permil (parts per thousand) already accounts for the 103 factor (Coplen, 2011). Additionally, when the delta value of a substance is more positive than the reference standard (or another sample) that indicates relative enrichment in the heavy (i.e., rare) isotope. Conversely, a more negative delta value indicates depletion of the heavy isotope. Stable isotope partitioning or fractionation between materials and phases can be classified broadly as resulting from equilibrium and kinetic mechanisms (Dawson & Brooks, 2001). In terms of equilibrium thermodynamics, the isotope exchange chemical reactions are regarded in the way that isotopes of a single element are exchanged between two substances until equilibrium is attained (Sulzman, 2007; Criss & Farquhar, 2008; Sharp, 2017). Equilibrium isotope exchange between two substances (A and B) is quantified using the alpha “” value, which is defined as  A-B = (1000+A) / (1000+B) Eq. 6 Where A and B, are the stable isotope compositions, respectively, of substances or phases A and B. In methane stable isotope applications, the α-value is used to express partitioning of 13C 19 and 12C between CO2 (or CO2 or DIC) and CH4 as a result of isotope effects associated with CO2/H2 methanogenesis ( Unkovich, et al., 2001; Criss & Farquhar, 2008) according to: α CO2-CH4 (13C,12C) = (1000 + 13C(CO2))/ (1000 + 13C(CH4)) Eq .7 Notably, isotope fractionation due to microbial metabolism typically is regarded as a kinetic isotope effect and for methanogenesis, a mixed kinetic and equilibrium isotope effect (Ferry, 1993). Consequently, use of α-values for this purpose commonly includes a caveat that the α-value is to be considered an ‘apparent fractionation factor’ rather than conventional equilibrium isotope partitioning (Chanton et al., 2006). Stable isotope fractionation resulting from kinetic isotope effects or simple partitioning between two materials commonly is characterized using capital (or ‘big’) delta “Δ” notation, which is defined as: ΔA-B = δΑ − δΒ Eq. 8 Where A and B, are the stable isotope compositions, respectively, of materials A and B. 1.5 Stable isotope measurement techniques Mass spectrometry is the earliest and most common technique for measuring stable isotope ratios in materials, a technique which dates back to the early 20th century (Thomson, 1913; Craig, 1957). The isotope ratio mass spectrometry (IRMS) instrumentation used to measure stable isotope ratios consists of two basic types: dual-inlet (DI-IRMS; Urey, 1948; McKinney et al., 20 1950) and continuous flow (CF-IRMS). Both types have four essential elements including an inlet system, an ion source, an analyzer (fixed sector magnetic) for ion beam partitioning, and detectors (Faraday cups) for ion beam collection (Dawson & Brooks, 2001; Sulzman, 2007). In both systems, samples are converted to a gas and two gas streams entered to the mass spectrometer via the inlet system by a set of capillaries. Capillary tubes are useful to assure no mass separation occurs during introduction of the gases into the IRMS source chamber (Brand, 2004). Then, gases are connected to a switch unit called a “changeover valve” which allows continued flow of the sample gases and reference gas to the ion source and waste vacuum line (McKinney et al., 1950). The ion streams are measured distinctly from both gases and compared a number of times (Werner & Brand, 2001). The measured relative differences in ion stream ratios is calculated relative to an internationally agreed isotope ratio scale (Coplen, 2011). Conventional analysis of carbon isotope of atmospheric CH4 has required large quantities of air (~100 L) in order to purify CH4 (from air) to acquire sufficient CO2 (after combustion of CH4) for measurements (Lowe et al., 1991). CF-IRMS pre-concentration systems have reduced the required amount of air to ~100 ml which is used to convert 2 ppm of CH4 from air samples into CO2 for direct entry into the source of the IRMS (Sulzman, 2007). Measuring 2H values at 2 ppm CH4 is even more challenging than 13C values because of the need to remove water vapor (up to several %) and molecular H2 (present at ~500 ppb in air) prior to combusting CH4 at 1000C to H2O and then reducing the H2O to H2 for stable isotope analysis. For CF-py-IRMS the CH4 is separated by gas chromatography and then converted by pyrolysis at 1440C to H2. Small amounts of either water or H2 that are not from CH4 create large errors in the measurements (Hilkert et al., 1999). 21 Generally, IRMS is inconsistent with a sticky molecule or a condensable gas like water (vapor); therefore, transformation of the water isotope ratio to a suitable molecule for analysis was necessary (Kerstel & Gianfrani, 2008). Further, the traditional IRMS converted methane to either CO2 (carbon isotopes) or H2 (hydrogen isotopes) (Craig 1953; Dawson and Brooks 2001; Sulzman 2007) and consequently, the system does not differentiate between 13CH4+ and 12CH3D+ (Stolper et al., 2014). Furthermore, all non-CO2 gases must be removed from carbon dioxide gas and oxidized before analysis in IRMS (McManus et al., 2002). More than 20 years ago, cavity ring-down spectroscopy (CRDS) was developed as a laser absorption technique having the high sensitivity potential for the quantitative detection of atomic and molecular species (O’Keefe & Deacon, 1998; O’Keefe et al., 1990). The most successful instrument for determining the stable isotopes of CH4 for both C and D was proposed by Bergamaschi et al. (1994), which resulted in the development of a high precision measurement method based on a Tunable Infrared Diode Laser Absorption Spectrometer (TIDLAS) under the commercial brand name of “Aerodyne”. Other commercial measurement systems (i.e., Picarro, Los Gatos Research) can measure only 13C values of CH4 at 2 ppm and higher. Measuring the 13CH4/12CH4 and 12CH3D/12CH4 ratios in TIDLAS technique is based on the intensities of infrared absorption lines of the corresponding isotope pairs. Benefits of the laser spectrometer instrument include minimal sample preparation (in some cases drying) and elimination of the need for conversion of CH4 to CO2 and H2 enabling direct measurement and a short analysis time (Bergamaschi et al., 1994). 22 1.6 Stable isotope applications Stable isotopic studies of CH4 have been used to improve understanding of the global CH4 cycle via determining the relative fluxes from different emission sources to establish the origins of increases in the atmospheric CH4 mixing ratio (Quay et al., 1988; Stevens, 1993). Both stable carbon (13C/12C) and hydrogen (2H/1H) isotope ratios can be employed as important constraints in the development of CH4 budgets through isotopic-weighting of fluxes from different sources; however, a lack of 2H(CH4) data continues to limit the extent to which stable hydrogen isotopes can be used for this purpose (Stevens & Rust, 1982; Lansdown et al., 1992; Whiticar, 1993, 1999). Stable isotopes also are important tools for characterizing different processes that produce, consume and transport methane (Barker & Fritz, 1981; Coleman et al., 1981; Martens et al., 1991) as a consequence of significant kinetic isotope effects (KIEs) associated with microbial metabolism and molecular diffusion (Chanton, 2005; Conrad, 2005). 1.6.1 Methane emission sources and processes Methane emissions from natural wetlands exhibit large variability in flux amount and stable isotope composition due to the wide range of environmental conditions under which methane is produced through anaerobic decomposition of organic substances, oxidized aerobically by methanotrophic bacteria and transported to the atmosphere via ebullition, molecular diffusion and through the stems of plants (Whiticar et al., 1986; Chanton, 2005). In freshwater ecosystems, methane production occurs in shallow sediments where substrate supply is more abundant and temperature during ice-free seasons typically is higher than depth. Fluxes and stable isotope compositions of CH4 measured at field sites and from soil incubations have been separately linked with local natural and ecological factors including temperature, water level, nutrient supply and 23 organic matter, characteristics of sub-layers, vegetation structure, oxidation and reduction potential, and biomass and carbon exchange (Kankaala et al., 2007; Walter et al., 2008; Hodgkins et al., 2014). However, factors affecting fluxes of methane are not independent and different variables (e.g., precipitation, Shingubara et al., 2019; substrates, Shannon & White, 1996; temperature, Crill et al., 1988; and plant communities, Whiting & Chanton, 1993) that serve as environmental integrators of CH4 production and consumption processes may be more successful predictors of fluxes and their stable isotope compositions (Moore & Knowles, 1990; Whalen & Reeburgh, 1992; Hornibrook et al., 1997, 2000; Whiticar, 1999; Whiticar & Schaefer, 2007). Furthermore, the link between vegetation type and methane production mechanisms has been proven in bogs dominated by Sphagnum sp. with the primary pathway being CO2 reduction, and in fens by more Carex sp., acetate fermentation being the predominant mechanism (Chanton & Whiting, 1996). There are four principal mechanisms leading to methane production in nature, consisting of “biogenesis”, “thermogenesis”, “geogenesis” and “ignigenesis”. Biogenesis refers to the end product of microbial metabolism (Whiticar et al., 1986; Burke et al., 1988; Ferry, 1993); thermogenesis is the thermochemical degradation of organic matters at elevated pressure and temperature, typically occurring during burial (Rice & Claypool, 1981; Schoell, 1980); geogenesis is the interaction between fluids and chemically reduced rocks (e.g., serpentinization reactions) at higher temperatures deep in Earth (Lollar et al., 2002); and ignigenesis refers to the byproduct of combustion of organic matter (e.g., wildfire or fossil fuel combustion) (Valentine et al., 2004). Methane in wetlands is produced exclusively by biogenic processes and consequently, methanogenesis in such systems is regulated primarily by temperature, substrate quality and factors that control oxygenation, such as water table levels. Anaerobic decomposition of organic 24 matter requires a complex microbial community of at least three interacting metabolic groups including fermentative, hydrolytic, acetogenic and methanogenic microorganisms, which produce CH4 and CO2 as metabolic endproducts (Hornibrook et al., 2000). Methanogens are a group of microorganisms that produce methane through two main metabolic processes: a) fermentation of acetate yielding methane and carbon dioxide by acetotrophic archaea (eq. 9), and b) reduction of carbon dioxide coupled to oxidation of H2 by hydrogenotrophic archaea (eq. 10) Chanton et al. 2005; Lai 2009): CH3COOH → CH4 + CO2 Eq. 9 4H2 + CO2 → CH4 + 2H2O Eq. 10 Soil pH is an important factor in determining the metabolic pathway of methane generation (Hartman et al., 2008) because of its influence on dissociation of acetic acid (i.e., whether acetate is bioavailable to acetotrophic methanogens) and the competitive advantage that it provides to microorganisms that tolerate a greater range of pH conditions (Segers, 1998). For example, under acidic conditions homoacetogenic bacteria that generate acetate from CO2 and H2 typically have a competitive advantage over CO2/H2 methanogens although the process eventually facilitates methanogenesis through production of substrate for acetotrophic methanogens (Lai, 2009). In general, the shallow subsurface of peatlands, which hosts the root zone of wet-soil adapted plants, is rich with labile organic carbon compounds that provide substrate for acetoclastic methanogenesis via degradation by other microorganisms higher in the anaerobic chain of decay in contrast to deeper peat layers which are substrate poor and dominated by H2/CO2 methanogenesis (Hornibrook et al., 1997). However, the low pH (<4) of acidic bogs provides an 25 exception to this generalization, resulting in a prevalence of H2/CO2 methanogenesis at all depths in northern Sphagnum-rich ombrotrophic wetlands, in some cases leading to a process that has become known as ‘acetate-decoupling’ (Hines & Duddleston, 2001; Hines et al., 2008). The absence or minimal use of acetate by acetotrophic methanogens yields methane having unusually negative 13C(CH4) values, which provides a means to distinguish CH4 emissions from the two key northern types of wetlands: ombrotrophic bogs and minerotrophic fens (Whiticar et al., 1986; Revesz et al., 1995). Gas transport processes can impart secondary kinetic isotope effects on the primary isotope compositions in CH4 that result from metabolic pathways. Methane transport from soil to the atmosphere occurs by three main processes in wetlands: molecular diffusion, ebullition and submerged and emergent plants (Kipphut & Martens, 1982; Martens et al., 1992; Walter et al., 2008; Lavoie et al., 2010). Molecular diffusion in wetland areas can arise from the surface of soil or water by diffusion of CH4 through the gas within plants (Martens et al., 1992). In the diffusion of methane between air and water interface, the fractionation is minimal; however, slow diffusion of methane through the aerobic zone makes it susceptible to bacterial oxidation, which leads to preferential consumption of 12CH4 and a position shift in 13C(CH4) values (i.e., 13C enrichment) (Chanton et al., 1992; Chanton, 2005). In bubble ebullition, CH4 transportation happens via rapid non-fractionating process (13C(CH4) in soil, so it may appear as 13C depleted in the atmosphere (Happel et al., 1994; Chanton, 2005). Transportation of gas from aquatic plants occurs via three modes: a) effusion (free-molecular), b) viscous (bulk or convective flow) and c) diffusion, of which the second process does not result 26 in a molecular weight discrimination or isotopic fractionation (Mason & Marrero, 1971; Dacey, 1987). Effusive or diffusive transport of CH4 through plant aerenchyma typically leads to preferential release of 12CH4 (i.e., CH4 that has more negative 13C values; Chanton, 2005). 1.6.2 Hydrogen isotope systematics of methanogenesis from wetlands Another useful constraint for a global CH4 flux budget is the isotopic abundance of deuterium in atmospheric methane and its sources (Whiticar et al., 1986). Anaerobic microbes consume CO2 and H2, or acetate, the main substrates for CH4 production in the natural environment, to produce CH4, while hydrogen is a key material for the community producing methane (Whiticar & Schaefer, 2007). Deuterium is also a useful way of distinguishing between freshwater and marine-sourced bacterial methane since bacterial CH4 formed in freshwater sediments is strongly depleted (-318‰) in deuterium, while marine bacterial is close to the mean value (-192 ‰) for D(CH4) (Whiticar et al., 1986). Further, changes in the two major pathways of methane production in freshwater ecosystems, acetate fermentation and reduction of CO2, have been reported as the relationship with inverse or antipathetic shifts in D and 13C of CH4 (Burke et al., 1988; Hornibrook et al., 1997; Chanton, 2005). The fermentation of acetate will lead to CH4 that is 13C enriched and D-depleted compared to CH4 produced via the CO2 reduction pathway which can be due to the mechanism of methanogenesis (Waldron et al., 1999). Moreover, D(CH4) values from wetlands exhibit a strong correlation with D of local precipitation so this constraint is latitude dependent which can help distinguish tropical and arctic wetlands CH4 sources (Waldron et al., 1999). The correlation between latitudinal gradient D(CH4) and pore water D(H2O) indicated that released methane reveals the D value of its source water (Chanton et al., 2006; Waldron et 27 al., 1999). Moreover, the fossil methane has a different D value rather than wetlands, which is helpful in the source detection of atmospheric methane (Stevens, 1993; Wahlen et al., 1990). The global average D for atmospheric and biogenic CH4 from wet environments are reported as -82 ‰, and -290 to -360 ‰, respectively (Wahlen et al., 1990). 1.6.3 Factors driving differences in 13C values of wetland CH4 emissions The highest rates of methanogenesis in wetlands occur in shallow peat in a zone that lies between the water-air interface (i.e., the water table level) and the lower depth of the root zone (Clymo & Bryant, 2008). This shallow zone also coincides with the largest temperature shifts annually and the highest temperatures during summer months. The latter promotes high rates of anaerobic decay and methanogenesis. The depth of the upper limit of the zone fluctuates throughout the year as a result of changes in precipitation inputs and losses due to evaporation and evapotranspiration. The lower depth is determined primarily by the types of plants that are present and in some cases peat depth. Differences in water chemistry and trophic status between wetland classes results in significant shift in the types of vegetation that are present, in particular, for fens and bogs. Minerotrophic fens host a range of deeply rooted graminoids, such as Carex spp. and Eriophorum spp., which results in an active rhizosphere zone to depths of 50 cm and greater (Clymo and Pearce, 2008). The graminoids release a variety of root exudates that provide labile substrates that are decomposed by anaerobic bacteria and methanogenic archaea leading to formation of CH4 and CO2 as terminal carbon mineralization endproducts. A rhizosphere rich in labile organic compounds, such as acetate and ethanol from exudates, in fens leads to a predominance of CH4 production by the acetate fermentation pathway (Chasar et al., 2000). In contrast, the dominant 28 vegetation type in acidic bogs is Sphagnum mosses, which are bryophytes that do not possess root systems. The low pH of the bog water coupled with an absence of low molecular weight exudates leads to a predominance of CH4 production by CO2/H2 methanogenesis (Lansdown et al., 1992). Methane produced in the shallow active zone in both fens and bogs has a short turnover time of <25 days (Lombardi et al., 1997) during which time it is either emitted to the atmosphere or consumed by methanotrophic bacteria that reside at the air-water interface or on the surface of plant roots. Methane also is produced at greater depths in peatlands at slower rates, resulting in the concentration of dissolved CH4 increasing substantially with depth commonly reaching the saturation limit of ~1,500 umol l-1. However, the turnover time for the deeper CH4 is much longer because the CH4 escapes the deeper, higher density peat primarily by molecular diffusion. Methane in porewater at depths >1 m contributes to emissions largely via exceptional events such as ventilation of accumulations of gas bubbles (e.g., Glaser et al, 2004). 1.7 Research statement 1.7.1 Problem and significance Methane concentration has increased in the atmosphere since the beginning of the industrial revolution or even the beginning of the 20th century (Khalil & Rasmussen, 1987; Chappellaz et al., 1990). Wetlands are the largest natural sources of methane emissions (~40%) (IPCC, 1995a) and approximately more than 50% of the total wetland area globally is located in the northern hemisphere between 40 and 70oN, covering the ecological regions of the cool temperature, boreal, and subarctic forest zones (Melton et al., 2013). The large variances in the flux estimates from wetlands in this zone are the result of differences in the estimated coverage of wetlands, and the duration of the flux period (Roulet et al., 1992). Wetland types vary according to complex gradients 29 of hydrology, nutrient availability (in particular, nitrogen and phosphorous), acidity (hydrogen ion activity), alkalinity (primarily bicarbonate concentration) and base cation abundance (Zoltai et al., 1975). Fens have a pH between 5.5 (moderate-rich fen) to 7.0 (extreme-rich fen) while Sphagnum-dominated bogs are acidic, mainly due to production of humic acid during decay processes and cation exchange from uronic acid production from Sphagnum (Zoltai & Vitt, 1995). These various biogeochemical characteristics of wetlands contribute to different stable isotope values of CH4 produced and emitted to the atmospheric (EPA, 2010; Bergamaschi et al., 2007; Bousquet et al., 2006). More specifically, CH4 emissions from fens and bogs can be distinguished isotopically because each type of wetland has its own biochemical pathways of methane production and transport that link to differences in water chemistry and trophic status (Hornibrook and Bowes, 2007). Therefore, preparing a comprehensive database on 13C-CH4 values from boreal wetlands can be a useful tool to track the role of northern wetlands in the global CH4 budget. The response of wetlands to global change has been uncertain (Moore et al., 1998) and understanding the response of the natural system to climate change and their natural variability in emissions is necessary because human emissions are superimposed on that natural background, which includes wetlands (Bartlett & Harriss, 1993; Cao et al., 1998). In order to monitor effectiveness of efforts to mitigate emissions of anthropogenic sources of greenhouse gases it is necessary to characterize and understand emissions from natural sources. Stable isotopes can be used to attribute changes in emissions to specific natural and human sources. The province of Manitoba, Canada was selected as the study area for refining the attribution of 13C(CH4) values to emissions from wetlands because a considerable proportion of Manitoba (43%) is covered by wetlands (Halsey et al., 1997) and all five Canadian wetland classes (fens, bogs, marshes, swamps, and open shallow water) are present in the province (National Wetlands 30 Working Group 1997). Remote sensing data are readily available for the province and considerable field data characterizing wetland types exists already in published and gray literature resources to ground truth new approaches to improve remote sensing delineation of bogs and fens, which are known to have distinctive 13C(CH4) emission signatures. There also have been several past studies of the stable isotope composition of methane from wetlands in Manitoba (Roulet et al., 1992, 1994; Bubier et al., 1995, 1999; Bellisario et al., 1999) and the region is of interest from a global change perspective because of the potential for permafrost wetlands in northern Manitoba to be degraded, expand both the area and CH4 emission strength of wetlands in this region of Canada (Bellisario et al., 1999; Halsey et al., 1997). 1.7.2 Goals and objectives The overarching goal of this study is to improve characterization of 13C values of CH4 emissions from boreal wetlands, in particular, bogs and fens because of their distinctive 13C(CH4) values. The approach for achieving this goal is to first develop a method for conducting a comprehensive probabilistic inventory of wetlands in Manitoba that has the potential to be extended to the boreal zone globally to quantify the relative areas of bogs and fens. The areas are required to estimate the relative contributions of these wetland types to the global CH4 budget to evaluate variability and assess the net 13C value of CH4 emissions from boreal wetlands. The approach will be applied in Manitoba using remote sensing data to characterize the areal coverage of different wetland classes combined with published data reporting CH4 emission strength and 13C(CH4) values for bogs and fens, which are the dominant wetland types in the study region. 31 Emissions from marshes and swamps were not included in the upscaling because their cumulative areas are small relative to bogs and fens in the province of Manitoba, and to date, distinctive 13C values have not been attributed to CH4 emissions from either class of wetland. The surface area of open water is large in the study area; however, the majority of large water bodies typically are aerobic due to wind action mixing surface water, which results in efficient removal of CH4 via bacterial methanotrophy during transport through the water column. Regardless, the littoral zone and shallow small water bodies can be significant sources of CH4 flux and it is acknowledged that a lack of published data about CH4 emission rates and 13C values for open water bodies in the boreal zone is a potential source of error in this study. Specific objectives are: • Use remote sensing data and a machine learning algorithm to classify different types of wetlands in Manitoba based on the highest probability of five different types of wetland including bog, fen, marsh, swamp and open/shallow water. • Attribute CH4 emission strength and 13C values for CH4 to bog and fen areas, which are the two dominant wetland classes typically located in the boreal zone. • Upscale the isotope-weighted CH4 emissions from bogs and fens to determine an integrated 13C(CH4) composition for the boreal zone in Manitoba. • Assess the variability of 13C(CH4) values for different ecozones/ ecodistricts in Manitoba due to natural differences in the relative groundcover of bog and fens in different parts of the province. • Compare the determinations of 13C(CH4) values for wetland emissions in Manitoba to 13C(CH4) values currently attributed to boreal zone wetlands in global CH4 budgets. 32 Chapter 2: Predictive wetland modelling in Manitoba, Canada using Google Earth Engine 2.1 Synopsis An overarching objective of this study is to improve representation of the areas of wetlands, in particular, fens and bogs in the boreal zone for the purpose of more accurately estimating 13C values of CH4 emissions to the atmosphere. This chapter reports a comprehensive probabilistic inventory of wetlands in Manitoba that was developed using satellite data and ancillary data about vegetation structure and environmental conditions. Synthetic aperture radar (SAR) and multi-spectral data from Sentinel-1 and Sentinel-2 for the year 2018 were acquired and processed in Google Earth Engine. The occurrence and type of wetland were predicted by combining the satellite data with information about vegetation structure, pH and topographic derivatives in a random forest model. The approach was able to distinguish different wetland types with a high overall predictive capacity (0.86), demonstrating the potential for combining optical variables with other metrics to model wetland occurrence at regional scales. 2.2 Background Wetlands are dynamic land areas over space and time (Wulder et al., 2018) and are amongst the primary natural sources of atmospheric CH4 emission contributing over 20% of the global CH4 produced annually (EPA, 2010; Bergamaschi et al., 2007; Bousquet et al., 2006). Therefore, mapping and monitoring wetland areas is critical for evaluating potential changes in future emissions of methane, which are a key contributor to global climate change (Melton et al., 2013; 33 Ozesmi & Bauer, 2002). While the emission strength and -values of CH4 emitted from fens and bogs are broadly known, one of the challenges for upscaling that information is a lack of high-resolution maps of fens and bogs in boreal regions. Thus, quantifying the spatial variability in wetland type and extent is require to develop more accurate estimates of atmospheric CH4 from wetlands and how those emissions might change as a result of global change. The prevalence of wetlands in the boreal zone of Manitoba has led to past interest in relationships between the spatial extent of different types of wetland and geographically variable emissions of CH4 (Bellisario et al., 1999; Bubier et al., 1995). The main goal of this chapter is to develop a new high spatial resolution and comprehensive inventory of wetlands in Manitoba based upon remote sensing data validated against ground-based data resources available for the province. Specifically, the goal is to define the area of fens and bogs in the province, which are dominant wetland classes in both Manitoba and the boreal zone more generally. Methodology developed in this chapter to identify bogs and fens from remote sensing data can be applied more widely in future work to characterize these wetland classes in the boreal zone globally. Amongst the various ecosystems on Earth, wetlands have been a focus for delineation and characterization using remote sensing techniques (Amani et al., 2019; Hermosilla et al., 2015; Hird et al., 2017; Kaplan & Avdan, 2017; Matasci, et al., 2018) in part because of international efforts to protect and preserve these ecologically significant and diverse environments (Frazier, 1999). Methods for detecting, mapping, monitoring and studying Earth’s systems and environments from remotely sensed datasets have dramatically improved due to increasing open-access satellite data, the development and use of machine-learning algorithms, and increasing availability of cloud computing including Google Earth Engine (Hird et al. 2017). Google Earth Engine (GEE) has facilitated preprocessing and classifying large amounts of satellite images, enabling facilitated 34 planetary-scale-geospatial analysis on a variety of communal issues involving disaster, drought, deforestation, water management, climate monitoring and environmental protection (Gorelick et al., 2017). Similarly, GEE provides a platform for mapping and monitoring wetlands over large areas such as the north of Canada (Amani et al. 2019). Wetland detection and monitoring are commonly conducted using combinations of multi-spectral (MS) sensors, which acquire information in several spectral bands (visible and near infrared, shortwave infrared, and thermal infrared), and synthetic aperture radar (SAR) (Gallant, 2015; Gosselin et al., 2014). Multispectral data are beneficial for distinguishing and classifying different types of wetlands (Dronova 2015; Gallant 2015) and SAR, an active remote sensing technology, has been employed to identify the extent, location and conditions of wetland areas (Ferreira-ferreira et al., 2015; Henderson & Lewis, 2008; Hong et al., 2015). The usefulness of SAR in mapping the extent of moisture on forested floodplains (Hess et al., 1995) and delineating wetland and non-wetland vegetation (Kasischke & Bourgeau-Chavez, 1997) is well established. Another use of remote sensing is to characterise the structure and function of ecosystems over extended areas. In recent decades, mapping tree species has been conducted using optical remote sensing (i.e., multi- and hyper-spectral imagery; Kim et al. 2009). However, a limitation of mid-resolution optical remote sensing approaches is the similarity of spectral characteristics among different tree species (Boschetti et al., 2007; Immitzer et al., 2012; Kim et al., 2009). These kinds of difficulties have decreased significantly due to the use of light detection and ranging (LiDAR), which is an effective means for quantifying forest structure attributes (Coops et al. 2007; Kim et al. 2009), such as canopy height and structure (Lefsky et al., 2002; Lim et al., 2003). LiDAR is an active remote sensing technology that uses a rapidly firing laser to measure the time it takes for the energy to be reflected off an object on the Earth’s surface and return to the sensor. The time is 35 then used to attribute the location (i.e., point) with an xy location and other relevant information (e.g., return number, intensity) resulting in a 3D point cloud from which relevant forest and vegetation structure attributes can be characterized (Hudak et al., 2014).However, forest structure attributes derived from LiDAR data are often spatially constrained due to limited airborne lidar collection campaigns (Matasci et al., 2018). New approaches that combine LiDAR plot data and Landsat multi-spectral data have been used to characterize forest structure, physical characteristics and heterogeneity across Canada (Matasci et al., 2018). As described in the Canadian Wetland Classification System, characteristics of vegetation structure and composition differ among wetland classes, forms, and types. The structure of vegetation is based on the tallest strata found in a wetland or residing at the deepest portion or central zone of the marshes and shallow open waters (National Wetlands Working Group, 1997). Additionally, vegetation structure is representative of hydrological processes, as taller vegetation strata are generally observed in shallower wetland types (e.g., swamps tend to have taller trees from the same species found in fens and bogs, which contain deeper water). Notably, vegetation and plant species in each wetland type are indicators for environmental characteristics such as water levels, pH, acidity, salinity and nutrient availability (Zolti &Vitt 1983), which are key attributes used to define different wetlands class that in turn are known to have different CH4 emission characteristics. In this study, synthetic aperture radar and multi-spectral data from Sentinel-1 and -2, (part of the Sentinel mission developed by the European Space Agency), were combined with vegetation structure information (Matasci et al. 2018), pH (Hengl et al. 2014) and topographic data to classify and delineate bog, fen, marsh, swamp and shallow open-water wetlands of Manitoba. 36 2.3 Study area The province of Manitoba, Canada covers 649,950 km2 with a widely varied landscape hosting thousands of lakes, including Lake Winnipeg, one of the world’s largest inland bodies of freshwater (Figure 2). More than two-fifths of the province’s land area is forested (McLintock & Bumsted, 2019). Most grasslands in the southern part of the province have been transformed into agricultural lands. In the river valleys Quercus spp., Salix spp., and populus spp. are present while most of the southern plains are unwooded. Picea glauca, Pinus banksiana, Populus spp., Betula papyrifera, and Larix laricina represent the dominant tree species in the north. A considerable part of Manitoba (~233,340 km or 43%) is covered by wetlands containing all five Canadian wetland classes (fens, bogs, marshes, swamps and open shallow water), of which approximately 90% are peatlands (Halsey et al., 1997). Permafrost bogs are common in northern Manitoba, while the southern areas are dominated by permafrost-free bogs and fens. The surficial geology is an important determinant of wetland type as bogs are commonly found in acidic bedrock while fens are found in calcic formations (Halsey et al., 1997). In addition to the drainage characteristics of soil, the extent of wetlands is correlated strongly with mean annual temperature, annual precipitation and humidity (Welsted, 1996). In general, development and persistence of wetlands in a landscape relies strongly upon temperature and precipitation, which are strongly influenced by seasonal and annual climate variability (Welsted, 1996). In Manitoba, the minimum and maximum annual average temperatures range from -30°C to 27°C and precipitation ranges from 70 to 110 mm annually with most rainfall occurring in June (Environment Canada, 2019). 37 Figure 2. The study area is the province of Manitoba, Canada. Ecozones of Manitoba are shown in the inset image. 2.4 Data Google Earth Engine (GEE) was used to process and acquire synthetic aperture radar (SAR) and multi-spectral remote sensing data for the wetland analysis. The public data catalog of GEE is comprised of remote sensing information from different sources including the full Landsat record, and a complete archive of data from Sentinel-1 and Sentinel-2 (Google, 2019; Gorelick et al., 2017). Imagery was used from Sentinel-1 and -2 (Table 4), which are part of the European Space Agency Copernicus program. The Sentinel-1 and -2 missions consist of high-resolution synthetic aperture radar and multispectral imaging, respectively. Sentinel-1 carries a C-band SAR sensor that allows all-weather and day/night imaging capability and has a temporal resolution of 12 days and a spatial resolution of 10 meters. The Sentinel-2 mission includes two satellites (A and B) 38 providing multi-spectral imagery with a temporal resolution of 5 days and a spatial resolution of 10 meters. Table 4. Summary of data used in this study. Data Set Derived Index Description Optical Imagery Blue (B2), green (B3), red (B4), near infrared (B8), normalized difference vegetation index (NDVI), normalized difference water index (NDWI) 10-m resolution Sentinel-2 optical satellite images from April to October 2018 were acquired for Manitoba, Canada. Radar Imagery Normalized polarization (NPol), vertical polarization (VV), VV standard deviation (VVsd) 10-m resolution Sentinel-1 polarimetric Synthetic Aperture Radar images April to October 2018 were acquired for Manitoba, Canada. LiDAR derived metrics Mean canopy height (m), standard deviation of canopy height (m), coefficient of variation of canopy height, total aboveground biomass (t/ha) Four vegetation structures were used from a model applied by Matasci et al. (2018). The metrics, derived from LiDAR and Landsat, were resampled to a 10 m resolution. Digital Elevation Models (DEMs) Topographic position index (TPI), topographic wetness index (TWI) 30-m resolution ALOS (Advanced Land Observation Satellite) images, provided by the Japan Earth observing program, were acquired for Manitoba, Canada and resampled to a 10 m resolution. Reference Data Wetland classification (bog, fen, marsh, swamp and shallow-open water) The current wetland inventory provided by the Government of Manitoba 39 2.4.1 Optical imagery data The multi-spectral imagery from Sentinel-2 used in this study was the B2 (blue), B3 (green), B4 (red) and B8 (near infrared) bands, and derived products including the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). These variable inputs were derived from Sentinel-2 imagery acquired between 1 April and 31 October 2018 and processed in GEE to provide noise-free images of Manitoba. The period between April and October was selected to acquire imagery that would the vegetation and hydrological dynamics of the different wetland classes. The multi-spectral bands (B3, B4, and B8) are necessary for determining NDVI and NDWI, which are of two most important indices in discrimination of wetland areas via remote sensing approach (Hird et al., 2017). The NDVI is a simple, but effective vegetation index for quantifying green vegetation. It normalizes green leaf scattering in the near infra-red wavelength and chlorophyll absorption in the red wavelength. The NDWI is commonly used identifying water bodies based on the reflected near infrared and visible green light (Borah & Sivasankar, 2018). Both NDVI and NDWI were calculated for each Sentinel-2 image using (McFeeters 1996; Sadeghi et al., 2017): NDVI = (Band8 – Band4) / (Band8 + Band4) Eq. 11 NDWI= (Band3 – Band8) / (Band3 + Band8) Eq. 12 2.4.2 Radar imagery data Sentinel-1 synthetic aperture radar (SAR) was processed through GEE and used to generate Normalized Polarization (NPol), Vertical Polarization (VV) and VV Standard Deviation (VVsd) variables from C-band Level-1 Ground Range Detected images at a 10 m spatial resolution (Hird 40 et al., 2017). VV and VVsd were extracted from series of VV-polarized images that included Sentinel-1 VV and VH data products covering Manitoba from 1 April to 31 October. Each pixel contains VV polarized backscatter (VVmean) and the standard deviation of VV (VVsd), which was calculated on a pixel-by-pixel basis from the SAR VV image stack. The selection of VV polarization in the present study was due to the wide availability of this parameter, as the Sentinel-1 satellites only gather VV or VV, VH C-band SAR over all observation regions but over the arctic or sea ice-covered terrain HH or HH, HV is collected (Pamaploni et al., 1997; Hird et al., 2017). NPol was chosen due to its utility for distinguishing vegetated and bare lands (Mattia et al., 1997), uneven surfaces, plant communities and dry biomass (Kornelsen & Coulibaly, 2013; Pope et al., 1997), as well as soil moisture detection (Becker & Choudhury, 1988; Patel et al., 2006). A second primary use of NPol is measuring plant parameters using the calculation of the co-polarization ratio (HH/VV), which is a parameter similar to NDVI, and the depolarization ratio (HV/VV), which is useful for quantifying canopy thickness (McNairn & Brisco, 2004). In this study, NPol was calculated from all VV and VH (vertical-vertical and vertical-horizontal polarization) images captured by the Sentinel-1 satellites from 1 April to 31 October 2018 using Equation (13): NPol = (VH – VV) / (VH+VV) Eq. 13 2.4.3 LiDAR derived metrics LiDAR sensors can be used to calculate specified locations on the landscape and bare surfaces on Earth by collecting x, y, and z measurements (O’Neil et al., 2018). This ability can be developed as a useful tool to derive information about wetlands (Hopkinson et al., 2006). LiDAR can sense 41 the surface morphology of vegetation (Richardson et al., 2010) and discriminate wet surfaces within peatlands (Touzi, 2007). It has been effectively used to characterize vegetation structure, which is a valuable feature in classifying different wetland types (Huang et al., 2014; Matasci et al., 2018; Millard & Richardson, 2014; O’Neil et al., 2018); however, there are some constraints due to the limited spatial extent of its application. Matasci et al. (2018) developed a model combining LiDAR plot data and Landsat satellite imagery to predict forest structure across Canada. In their model, a combination of forest characteristics (height, canopy cover, volume, and biomass) from LiDAR plots located across Canada's forested ecosystems and a time series of Landsat image composites, as well as spatial coordinate and terrain features, were used to provide spatially extensive, and temporally dense information on vegetation structure and dynamics in Canadian forests. In the present study, four derivatives from Matasci et al. (2018) representative of forest and vegetation structure are included in the analysis including i) canopy height (m), ii) standard deviation of canopy height (m) representing the standard deviation of canopy cover, iii) coefficient of variation of canopy height representing vegetation heterogeneity, and iv) total aboveground biomass (t/ha). 2.4.4 Topographic data Topography was characterized using the Advanced Land Observation Satellite Digital Surface Model, which has a 30 m resolution (Tadono et al., 2014). Two topographic indices were derived from the digital elevation model: i) the topographic position index (TPI), and ii) the topographic wetness index (TWI). TPI compares the elevation of a cell to the mean elevation of the surrounding cells in a neighborhood where positive values indicate the cell is higher than its surroundings (e.g., a ridge) while negative values are opposite (e.g., a valley) and flat areas have a value near zero 42 (Ågren et al., 2014; Hird et al., 2017). TPI values provide a simple and powerful means to classify the landscape into topographic position classes to characterize surface morphology (Laamrani et al., 2015; Tagil & Jenness, 2008). TWI provides a relative soil moisture index for a single cell and as a topographic index for distribution of soil surface wetness (Mckenzie & Ryan, 1999): TWI = ln ( / tan ) Eq. 14 Where,  is the slope of the catchment area contributing to the specific point per unit contour, and tan  is the local topographic slope (Tagil & Jenness, 2008). 2.4.5 Wetland reference and training data The Manitoba Land Initiative inventory provided by the Government of Manitoba (2017) was used for training and assessing the accuracy of the model (Figure 3). The wetland inventory of Manitoba contains the five typical wetland types that occur in Canada: bog, fen, marsh, swamp, and shallow water. This valuable inventory is released by Manitoba Habitat Heritage Corporation (MHHC) and is the first wetland dataset extending from the Manitoba-Saskatchewan border to the City of Brandon (Table 5). The wetland inventories in Canada have been conducted by the Canadian Wetland Inventory Technical Committee (CWITC)1. The CWITC developed a data model based on the main features defining wetland types including geometry, anthropogenic 1 The committee members include Canadian Space Agency, Ducks Unlimited Canada, Environment and Climate Change Canada, Natural Resources Canada, North American Wetland Conservation Council (Canada), Nova Scotia Department of Natural Resources, Ontario Ministry of Natural Resources Université de Sherbrooke, University of Victoria 43 impact, physiognomy, and soil classification. The feature types according to Canadian wetland classification system were bog, fen, marsh, swamp and shallow/open water (Canadian Wetland Inventory Technical Committee, 2016). Table 5. Summary statistics of the wetland proportions in the original data. Wetland Class Area (km2) Percentage (%) Bog 1010. 7 Fen 7544 50 Marsh 1502 10 Shallow / Open Water 1758 11 Swamp 3421 22 44 Figure 3. Wetland inventory in southern part of Manitoba, Canada. The gray color areas in Manitoba map, top right corner, show the available wetland inventory and identified red square shows the extent of the provided example. 2.4.6 pH data inventory An inventory of pH data has been included to help distinguish different wetland classes. SoilGrids is a comprehensive data bank providing global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm) at a 250 m resolution. Hengl et al. (2014) built a soil pH model based upon in situ soil data from ca. 150,000 field sites extending across all continents globally, which was combined with remote 45 sensing data to predict soil pH at a 250 m resolution. In the present study, surface pH values (i.e., at 0 cm) from SoilGrids (Hengl et al. 2014) was incorporated into the wetland delineation model. 2.5 Methods 2.5.1 Random forest classification model A Random Forests (RF) classification model (Breiman, 2001) was used to predict the probability of fens, bogs, swamps, marsh, and shallow/open water in Manitoba. The RF model uses bootstrap samples of the data and individual predictors to fit numerous classification trees that are then averaged and compared to an out-of-bag sample which is withheld to evaluate model performance (Cutler et al., 2007; Nelson et al., 2017). The RF model was developed using 65,274 random samples drawn from the existing Manitoba Land Initiative Wetland database. For each location, the wetland type (bog, fen, marsh, swamp and shallow/open water) was selected as well as values for the 16 covariates including B2, B3, B4, B8, NDVI, NDWI, VV, VVsd, NPol, TPI, TWI, canopy height, standard deviation of canopy height, coefficient of variation of canopy height, total above ground biomass and pH (Table 4, Figure 4). The RF model was fit using the ranger (Wright & Ziegler, 2017) and mlr (Bischl et al., 2016) 46 Figure 4. Flowchart of the methodology used to produce the probabilistic wetland maps for Manitoba, Canada. Highest probability classes of wetland, MB Optical Imagery Sentinel 2 Data download & pre-processing Bands 3&8 Bands 4&8 Bands 2,3,4&8 NDVI NDWI DEM ALOS Data download & pre-processing TPI, TWI SAR ENVISAT1 Data download & pre-processing Pol Bands VV & VVsd Sampling points from covariates Southern MB wetland inventory pH data inventory Running RF model Probability of Fen map Probability of Bog map Probability of Swamp map Probability of Marsh map Model interpretation Variable importance Partial dependence plots Prediction Probability of Open water LiDAR derived metrics -Mean height and SD of forest elevation - Forest elevation co-efficient of variation biomass Confusion matrix & sensitivity statistics 47 packages in R 3.5.1 (R Core Team, 2018). The RF model, composed of 2000 individual trees, was optimized by tuning hyperparameters for the number of covariates included in the model (range: 1 to 15), the sample fraction of data for each individual tree (range: 0.2 to 0.9), and the minimum node size (i.e., the number of wetland classes required in the individual tree nodes; range: 1 to 10) over 1000 iterations to minimize the prediction impurity. For each iteration, the RF model was compared to the out-of-bag sample based on a 5-fold spatial cross-validation where model predictions are drawn from wetland sample locations that minimize spatial autocorrelation (Brenning, 2005). Hyperparameter tuning, which minimized the out-of-bag error rate (0.29), resulted in 16 covariates, 0.41 sample fraction, and a minimum node size of 8. The final tuned RF model was used to predict the probability of each wetland class (bog, fen, marsh, swamp, and shallow/open water) at a 10 m resolution across Manitoba. 2.5.2 Evaluation of the model The RF model predictions and out-of-bag samples were compared using a confusion matrix, a technique for summarizing the performance of a classification algorithm. For this purpose, the model performance was evaluated using the classification (producer/user) accuracy, which is based on a comparison of the predicted wetland class in the classified map (each row) to the reference wetland class to evaluate whether they were correctly identified in the final probability maps of wetland types. Variable importance was assessed by evaluating the number of times a covariate was selected for splitting in the model and its overall contribution to the decrease in Gini impurity (a measurement of the probability of an incorrect classification of a new case of a random variable) (Buston & Elith, 2011). Summary statistics were calculated based on the area of each of the five wetland types relative to the total area of Manitoba (649,950 km2). 48 2.6 Results 2.6.1 Probabilistic wetland model The selected random forest model based on hyper-parameter tuning had an overall predictive accuracy of 0.86 based on comparisons of the model predictions and out-of-bag samples (Table 6). The producer accuracy (i.e., sensitivity) by wetland class ranged from 0.84 (fen) to 0.92 (shallow/open water) (Table 6). The user accuracy (i.e., precision) of the predicted classes ranged from 0.47 (marsh) to 0.97 (fen) (Table 6). From the training dataset, fens were the most commonly predicted wetland class and were most frequently confounded with swamps, bog, marsh, and open/shallow water, respectively, reflecting the lowest observed producer accuracy. Swamp (0.89), bog (0.90), and marsh (0.88) were the next most commonly observed and had similar producer accuracy. All three were most commonly misclassified as fen (Table 6). Shallow/open water had the highest producer accuracy (0.92) and was the least observed in the training dataset. Shallow/open water wetlands were most commonly misclassified as a marsh. Table 6. Confusion matrix based on the number of pixels for the RF classifier algorithm with the user accuracy, producer accuracy, errors of commission, and omission (in %). Prediction Bog Fen Marsh Swamp Shallow/open water Total User Accuracy Bog 3728 1985 27 132 2 5874 0.63 Fen 335 39100 114 829 40 40418 0.97 Marsh 43 1852 1892 151 94 4032 0.47 Swamp 29 3195 66 9479 14 12783 0.74 Shallow/ open water 4 382 60 29 1692 2167 0.78 Total 4139 46514 2159 10620 1842 65274 Producer Accuracy 0.90 0.84 0.88 0.89 0.92 Overall accuracy: 86 49 2.6.2 Prediction of wetland classes The outcome of the five probable wetland classes of Manitoba at a 10 m spatial resolution (Figures 5 to 10) confirms that bogs have a similar distribution throughout different ecozones of Manitoba and are most prevalent in the boreal shield (31%; ecozones are shown in Fig. 3), while fens have a different distribution with their greatest area in the boreal plain (48%) and lowest area in taiga shield (7%). Swamp and marsh have the smallest areas of 9% and 3%, respectively, of the five wetland types in Manitoba. Generally, shallow/open water is classified as the highest wetland area, occupying 74%, 55%, 43%, and 31% of the total areas of taiga shield, Hudson plain, boreal shield, and boreal plain, respectively in Manitoba. The summary statistics of this study indicated that 34% and 66% of the total area of Manitoba is covered by wetlands and non-wetlands, respectively. The major wetland class is shallow/open water (17%), and peatlands including bog and fen, significant carbon storage zones, cover a large area of Manitoba (15%). Swamp (0.9%) and marsh (0.2%) comprise the lowest portion of total land area in Manitoba. 50 Figure 5. Probability map of bog in Manitoba, Canada The white patches in the north-central region is due to missing data. 51 Figure 6. Probability map of fen in Manitoba, Canada The white patches in the north-central region is due to missing data. 52 Figure 7. Probability map of marsh in Manitoba, Canada The white patches in the north-central region is due to missing data. 53 Figure 8. Probability map of swamp in Manitoba, Canada. The white patches in the north-central region is due to missing data. 54 Figure 9. Probability map of shallow/open water in Manitoba, Canada. The white patches in the north-central region is due to missing data. 55 Figure 10. Probability of wetland classes in Manitoba using Random Forest Model. 56 In the wetland classification model, NDWI was the most effective variable, followed by NDVI, B8, NPol, B2, FELEV, FIRST2M, B3, VVsd, TWI, FIRSTAM, TPI, TBIOM, VV, and B4 (Figure 11). Overall, partial dependence plots for the top eight covariates in the random forest model revealed fens had clear functional relationships with covariates compared to the other wetland classes (Figure 12). Wetland class probability related to covariates was generally more similar for the marsh, swamps, and open/shallow water. Fen probability was highest for low NDWI values, and similar to marsh and swamp, was inversely related to NDWI (Figure 12). Fen probability was highest at moderate NDVI values and was indistinguishable from swamp at the highest observed NDVI values. Increasing values in the near-infrared (Sentinel-2 B8) clearly distinguished marsh from fen and water, while bog and swamp class probability were more related as near-infrared values increased (Figure 12). For the remaining variables, NPol, B2, pH, FELEV, and FIRST2M, fens had the highest predicted probability, followed by bogs, which remained consistent across the range of the covariates (Figure 12). 57 Figure 11. Variable importance for wetland probability model in Manitoba, Canada. The variables are Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Near Infrared Band (B8), Normalized Polarization (NPol), B2 (blue), Canopy height (FELEV), standard deviation of canopy height (FIRST2M), B3 (green), VV Standard Deviation (VVsd), topographic wetness index (TWI), coefficient of variation of canopy height (FIRSTAM), Topographic Position Index (TPI), Total Aboveground Biomass (TBIOM), Vertical Polarization (VV), B4 (red). 58 Figure 12. Partial dependence plots, which show the predicted wetland class probability based on a single covariate while holding all other covariates at their mean value, for each of the top eight covariates in the model. The variables are Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Near Infrared Band (B8), Normalized Polarization (NPol), B2 (Blue), pH, Canopy height (FELEV) and standard deviation of canopy height (FIRST2M). 59 2.7 Discussion Wetlands are globally important ecosystems providing habitat for numerous animal and plant species, as well as improving the quality of water resources (Guo et al., 2017). They also store large amounts of carbon and nutrients (Melton et al., 2013). A broad and precise inventory of wetlands is necessary for wetland conservation and environmental planning (O’Neil et al., 2019), as well as understanding potential emissions of methane, a key factor in climate change (Melton et al., 2013; Ozesmi & Bauer, 2002; Ringeval et al., 2010). In the present study, the random forest model had high overall predictive accuracy for wetland areas in Manitoba which reveals strong discrimination among wetland types and dry lands. From the model outputs, fens are the most dominant wetland class. Bog and fen are ecologically similar, even in the field, which is the main reason why these two types of wetlands are occasionally designated as the same class (e.g., peatland) (Grenier et al., 2007). Swamps, in our study area, are mainly covered by deciduous forest and some are misclassified as fens. According to the National Wetland Working Group (1997), treed swamps grade into treed fen where more water is available, which may explain the patterns of misclassification in the present study. Overall, treed wetlands tended to represent most of the misclassification errors, which may have led to the overestimation of this class (i.e., woody vegetation class; Ferreira-Ferreira et al., 2015). Parameters NDVI, NDWI and the near-infrared (B8) had the most influence on wetland class predictions. Both NDVI and NDWI have been reported as the most influential variables in determining wetland areas in different studies (Beijma et al., 2014; Hird et al., 2017; Kaplan & Avdan, 2017; Kaplan et al., 2019; Prasad et al., 2006). NDVI is an enhanced method for differentiating spectral features (Hui et al., 2009) and its significance can be explained by its high sensitivity to photosynthetically active biomes (Wylie et al., 2003). NDWI is an efficient index for 60 the contrast between water and non-water classes for wetland mapping (Ji et al., 2009), which had a major role in our study. Hird et al. (2017) reported NDVI and NDWI had a greater influence predicting wetland occurrence compared to surface roughness or soil moisture from their SAR model inputs in northern Alberta. NDVI has also been found to be the most important variable for identifying the vegetation extent of coastal salt marsh habitats (Beijma et al., 2014). The near-infrared band (B8) had an important role in discrimination of wetland types compared to the visible bands (i.e., blue, green and red) in the present study. B8 is expected to better discriminate vegetation properties and penetrate water features (Schmidt & Skidmore, 2003), which helps to distinguish vegetation/non-vegetation area and highlight gradients of vegetation structure along wetland classes. The importance of B8 is due to the high near-infrared reflectance of green vegetation, which increases in the red to the near-infrared wavelengths, providing a useful indicator for wetland presence (Adam et al., 2010; Cochrane, 2000; Schmidt & Skidmore, 2003). Consequently, near-infrared data are useful for identifying vegetation, quantifying biomass content of vegetation and its health, and distinguishing water bodies from land (Manevski et al., 2011). In the visible portion of the electromagnetic spectrum, the red band (B4) had the least influence on wetland class prediction. Generally, this band is sensitive to physical structure of plants (e.g., Leaf Area Index), physiological status of vegetation (e.g., chlorophyll amount), and the level of water availability in vegetation biomass (Filella & Penuelas, 1994; Clevers et al., 2002). Wetlands are dynamic ecosystems where hydrological regime can change rapidly (e.g., land filled with water) during short seasons or over long time periods (decades) (Prigent et al., 2001b). pH had moderate effect delineating fens, which are less acidic or even alkaline, compared to other wetland types. The pH of a fen is related primarily to its groundwater-fed systems (Zoltai & Vitt, 1995) which result in a strong functional role of pH in predicting fen from other classes in 61 the present study. Also, pH is an important determinant of bacterial communities in wetland soils, which is an important factor in methane production pathways (Hartman et al., 2008; He et al., 2019). However, there is uncertainty with pH in the model and these data are not specific to wetlands. Hengl et al. (2014) reported that all areas with no evidence of historical vegetation cover were excluded and thus, their prediction model for identifying wetlands is based only on vegetated areas (Hengl et al., 2014). To date, the role of pH in discrimination of wetland types has not been included in broad-scale remote-sensing studies examining the occurrence and distribution of wetlands in Canada (e.g., Hird et al., 2017; Wulder et al., 2019). A study conducted in Bulgaria indicated that pH had an important influence for classifying wetland vegetation diversity (Hájková et al., 2006). They reported a significant correlation between species richness and pH on arctic-alpine species; however, the boreal species richness was independent of pH, which is consistent with results presented here. Canopy height (FELEV) and standard deviation of canopy height (FIRST2M) had a moderate influence on classifying wetland types, in particular, for distinguishing fens and swamps from bogs, marshes and shallow/open water classes. The different vegetation structures of fens (i.e., dominated by graminoid species and brown mosses) versus swamps (i.e., a cover dominated by trees, shrubs and herbaceous flowering plants; National Wetlands Working Group, 1997) is the main reason for the effectiveness of canopy height (FELEV) and standard deviation of canopy height (FIRST2M) in the present study. Vegetation structure also helps to distinguish wetland classes in different ways. For example softwood tree species (e.g., Larix laricina) commonly found in wet and nutrient-rich soils are a good indicator for separating fens from bogs (Smith et al., 2007). Both vegetation height and species can be used to help classify wetlands. The vegetation structure of bogs (i.e., dominant 62 cover sphagnum mosses and ericaceous shrubs; National Wetlands Working Group 1997) is characterized by shorter shrub height (< 2 m) while swamps are associated with tall shrubs with average heights >2 m (Smith et al., 2007), which are useful indices for classification of wetland types using the related variables canopy height and standard deviation of canopy height in the present study. Above-ground vertical characterization provided by LiDAR has been used to capture and quantify vegetation structure in the Canadian boreal forest (Matasci et al., 2018), Canadian forest ecozones (Hopkinson et al., 2006), and Douglas-fir and western hemlock dominated forests on the eastern side of Vancouver Island (Coops et al., 2007). The addition of LiDAR-based vegetation metrics (Matasci et al., 2018) in this study improved wetland classification by helping distinguish wetland types based on their vegetation characteristics. In the present study, TPI and TWI were less important predictors of wetland classes, which is consistent with results from a study of wetlands in the boreal forest of Alberta (Delancey et al., 2019). However, topographic variables have been reported as important predictors of wetlands and other landscapes in numerous studies (Ågren et al., 2014; Hird et al., 2017; Laamrani et al., 2015). Topographic variables likely were not influential in the present study as a result of the coarse resolution (30 m) of the ALOS data, which may not have captured topographic variability that influences hydrological processes important for wetlands. This study shows that the total area of all five predicted wetland classes is 0.22×106 km2, which is consistent with previous studies that have mapped wetland type and distribution in Manitoba (0.23×106 km2; Halsey et al., 1997). These results and the successful upscaling of the RF model to the provincial level demonstrate the advantages of exploiting freely available Earth Observation data archives supported by cloud computing-based compilation and processing of data sets, and 63 the use of machine-learning algorithms for large-area mapping and monitoring of wetlands. The outcomes of the present study may be useful for monitoring and sustainable management of wetland resources in Manitoba and other provinces that host areas of boreal zone wetland habitat. 64 Chapter 3: Review of recent 13C data for wetland CH4 3.1 Synopsis The purpose of this chapter was to compile published 13C(CH4) data for wetlands published since the last review of similar data in 2009 for the purpose of assessing whether further refinement of 13C(CH4) values for bog (-74.9  9.8‰) and fen (-64.8  4.0‰) emissions is possible. The amount of new isotope data for CH4 emissions from wetlands globally was determined to be small and only limited new data are available for boreal wetlands. A key finding of the literature review is that recent measurements of CH4 emissions from northern regions that contain a mixture of fen and bog habitat exhibit integrated 13C values that agree well with previous assessments of 13C(CH4) values for fens and bogs. The outcome of this literature review and data assessment was a decision to use 13C(CH4) values for bogs and fens from the last comprehensive overview conducted in 2009. 3.2 Background Stable isotope ratios (13C/12C or 2H/1H) provide important constraints in global and regional CH4 budgets through ‘isotope-weighting’ of emission sources (Lansdown et al., 1992; Whiticar 1993, 1999; Hornibrook et al., 1997; Hornibrook et al., 2000). The approach involves assigning stable isotope compositions or ‘signatures’ to different sources and then applying fractionation factors to account for stable isotope partitioning associated with different processes or ‘sinks’ that remove CH4 from the atmosphere (Andersen, 1996; e.g., OH radical; soil methanotrophy). The net 65 result is that the isotope-weighted sum of emissions minus fractionation effects should equal the amount and stable isotope composition of CH4 in the atmosphere. One challenge with the isotope-weighting approach is accurately determining the stable isotope composition of CH4 emitted from different sources (Miller 2004). Improved representation of stable isotope compositions of wetland CH4 emissions in different global models has progressed slowly since the first isotope-weighted budgets were developed in the 1980s (Stevens and Engelkemeir, 1988). Commonly, a uniform stable carbon isotope composition has been assigned to wetlands globally (e.g., -58‰; Mikaloff-Fletcher et al., 2004a,b) or more rarely different 13C values have been attributed to high latitude (-70‰) and tropical (-55‰) wetlands because of perceived differences in CH4 production pathways, the quality of organic matter and average temperature of CH4 formation (Warwick et al., 2016). Isotope-weighting in modern CH4 global budgets has been based mostly on 13C values occasionally in combination with radiocarbon data and carbon monoxide and ethane concentrations to help distinguish fossil CH4 sources (Simpson et al., 2012). Stable hydrogen isotope data for CH4 emissions remain scarce and most emission 2H(CH4) values are estimated from belowground measurements of CH4 rather than direct emissions (Warwick et al., 2016). Despite data limitations, 2H(CH4) values have been used in isotope-weighted budgets to evaluate broad changes in the global CH4 cycle during the Holocene based upon air bubbles trapped in polar ice cores (Sowers 2010). Applications in modern CH4 budgets are relatively rare (Rigby et al., 2012; Sherwood et al., 2016) due to poor availability of data but also because there have been few advances in understanding underlying causes of variability in 2H values of CH4 emitted from wetlands with the exception of the influence of 2H/1H ratios in meteoric water on 2H incorporation into CH4 (Waldron et al., 1999; Chanton et al., 66 2006). The majority of isotope-weighted global CH4 budgets will continue to rely primarily upon 13C values until there are improvements in availability of 2H(CH4) data and better characterization of isotope effects associated with processes that produce, consume and transport CH4 in major emission sources (Feinberg et al., 2018; Ganesan et al., 2019). Characterising isotope signatures and isotope effects in wetlands remains important because the ecosystems are a dominant source in the global CH4 budget, accounting for ~170 million tonnes of CH4 emissions per year or ~25% of total global CH4 emissions (Forster et al., 2007). A recent review by Sherwood et al. (2016) of stable isotope values of CH4 from different global sources evaluated 13C values on the basis of average and median stable isotope values from all available sources. While the assessment of n=556 published measurements for wetland CH4 reported mean (-61.5‰) and median (-62.5‰) 13C values that are similar to values used in isotope-weighted budgets, the lack of consideration for wetland classes and biophysical processes did not advance the predictive capacity of isotope-weighted global budgets. Assigning static isotope signatures to a major widespread emission source, such as wetlands, does not allow evaluation of changes in daily, monthly and annual records of atmospheric CH4 measured in the extensive global network of monitoring stations (e.g., AGAGE; Cunnold et al., 2002). For example, use of static isotope signatures limits the ability to assess changes in CH4 flux strength from different regions dominated by particular types of wetlands (e.g., tropical swamps; boreal bogs and fens; Arctic tundra; the Amazonian floodplain; African papyrus swamps, etc.) or increases or decreases in CH4 flux due to shifts in environmental conditions related to human disturbance or climate change, in particular, processes that impart an isotopic fractionation on CH4 emissions. The last comprehensive analysis of 13C values of subsurface and emitted CH4 from different classes of wetland was conducted by Hornibrook (2009). It followed initial evidence suggested 67 by Hornibrook et al. (2000) that differences should exist in the 13C values of CH4 emissions from fens and bogs due to differences in microbial pathways responsible for CH4 production under high and low pH conditions. The difference in 13C(CH4) values results from methanogenesis occurring primarily by acetate fermentation in shallow peat in fens and by CO2/H2 methanogenesis in bogs. The impact of pH and trophic status on 13C values in subsurface porewater CH4 was first noted by Bellisario et al. (1999). Hornibrook and Bowes (2007) later confirmed in a study of four UK wetlands spanning a nutrient and pH gradient that a progressive 13C-depletion in CH4 from intermediate to low pH also is reflect in the 13C values of CH4 emitted to the atmosphere. The study also showed that a consistent offset of ~-12‰ exists between mean subsurface 13C(CH4) values and the stable carbon isotope composition of CH4 emitted to the atmosphere due to secondary isotopic fractionation associated with the relative transport rates of 12CH4 and 13CH4 during diffusion through plant aerenchyma (Chanton and Dacey, 1991; Chanton 2005). The purpose of this chapter is to compile stable isotope data for wetland CH4 published since 2009 to evaluate whether 13C values estimated by Hornibrook (2009) for CH4 flux from bogs (-74.9  9.8‰) and fens (-64.8  4.0‰) should be used, or refined further, in upscaling calculations in Chapter 4 that will examine 13C values of CH4 emissions based upon areas of fen and bog in Manitoba determined using the new remote sensing method reported in Chapter 2. 3.3 Data compilation Methane emission, flux and pore water 13C values from natural wetlands were compiled from peer-reviewed literature for the period spanning January 2009 to April 2020 through an inclusive search on the Web of Science database using the keywords “northern* AND peatlands*”, 68 “methane* AND emissions*”, “stable isotope* AND methane*”, “fens”, “bogs” and “wetland* AND flux*”. Also, all references in the papers found in the search were checked in order to be sure of a detailed coverage. In addition to 13C(CH4) values, the wetland type, location (latitude and longitude), and dominant vegetation cover for each site were documented if reported. Although this study is focused on fens and bogs in the boreal zone, all occurrences of stable isotope data for wetland CH4 were recorded; however, only data from wetlands situated at >45°N are included in the comparison to outcomes reported in Hornibrook (2009), which focused on high latitude bog and fens, and are relevant to wetlands in Manitoba, Canada. 3.4 Results The locations of the wetland sites for which stable isotope data were published since 2009 are shown in Figure 13. A summary of the wetland attributes is presented in Table 7. Data in the table are separated into latitudes of >45°N (high northern latitudes) and 40N to 40S (subtropical and tropical zones), consistent with the classification of wetland distribution reported in Ganesan et al. (2018), a study which created the first globally gridded data base of 13C values for wetland CH4 emissions. In the data comparison, wetland sites 3, 4, 5 and 13 were excluded because their location was <45°N. Data from site 7 also was not considered because EVV Upper Lake is a lake with no associated fen or bog and is located in a zone of continuous permafrost. 69 Figure 13. Global distribution of wetland sites for which stable isotope data for CH4 have been reported since 2009. The numbers correspond to labels in Table 7, which provides details about wetland location, type, dominant vegetation and publication source. 70 Table 7. Summary of global wetland studies since 2009 that have reported stable isotope data for CH4. Site Location Site name Ecosystem type Latitude Predominant Vegetation Reference 1 Komi Republic, Russia Seida Village Fen 67 03' N Salix spp. Marushchak et al. (2016) 2 Eastern Finland Salmisuo Pine Fen 62 47´ N Not reported Dorodnikov et al. (2013) 3 Georgia, USA Hammersmith Creek Tidal freshwater marsh 31 20' N Zizaniopsis miliacea Segarra et al. (2015) 4 Maine, USA (Acadia National Park Fresh water creek 44 24' N Not reported 5 Florida, USA Florida Coastal Everglades Fresh water marsh 25 49' N Cladium jamaicense, Eleocharis, Panicum spp. 6 Minnesota, USA Red Lake peatland Glacial Lake 48 N Sphagnum moss Carex spp. Corbett et al. (2013) 7 Kangerlussuaq, Greenland EVV Upper lake continuous permafrost 67 05'N Salix glauca, Vaccinium uliginosum Carex concolor, Calamagrostis lapponica, C. lapponica, Hippuris vulgaris Thompson et al. (2016) 8 Finland Sodankylä Open eutrophic flark fen 67 22´ N Sedge, birch Fisher et al. (2017) Fisher et al. (2017) Fisher et al. (2017) Fisher et al. (2017) Fisher et al. (2017) 9 Finland Lompolojänkkä Open nutrient-rich fen 67 59´ N Low shrubs and sphagnum 10 Finland Kaamanen Mesotrophic fen 69 08´ N Not reported 11 Finland Faerdesmyra Palsa mire around Lake Ferdesvatnet 69 43´ N Not reported 12 Sweden Stordalen Mire (multiple sites) Discontinuous permafrost 68 21´ N Sphagnum moss & graminoid spp. 13 Kyoto City, Japan Mizorogaike Pond Bog 35 03' N S. cuspidatum, R. fauriei, P. australis, M. trifoliate, S. palustre Itoh et al. (2015) 14a Sakha, Russia Sakha, Russia Sakha, Russia Verkhny Khatistakha Larch forest bog 70 15' N Larix gmelinii, Carex spp., Eriophorum angustifolium Shingubara et al. (2019) Shingubara et al. (2019) Shingubara et al. (2019) 14b Kodac Tiga-tundra boundary 70 34' N Larix gmelinii, Sphagnum squarrosum Eriophorum angustifolium 14c Boydom Low-centered polygon bog 70 38' N Larix gmelinii, Eriophorum angustifolium 71 The 13C data for sites 1, 2, 6, 8, 9, 10, 11, 12 and 14 were separated into subsurface CH4 dissolved in porewater (Table 8) and atmospheric CH4 flux (Table 9). Only sites 1 (Seida Village, Russia; Marushchak et al., 2016) and 2 (Salmisuo, Finland; Dorodnikov et al., 2013) reported 13C data for both porewater and emitted CH4. Environmental data report for each location differed in the number and types of parameters (e.g., pH, soil temperature, water chemistry, etc.). Subsurface 13C(CH4) values were reported for sites 1, 2, 6 and 14a-c. The depth of subsurface sampling ranged from 0 to 30 cm for sites 1 and 14a-c, 0 to 200 cm at site 2, and 0 to 300 cm at site 6 (Table 8). The sites in Russia were sampled across multiple years: 2007 and 2008 for Seida Village (Marushchak et al., 2016) and 2011, 2012 and 2013 for Sakha (Shingubara et al., 2019). The Sakha, Russia site is notable also because four types of Larix (Larch) wetland were sampled for the three years. The sites in Finland and USA were studied during a single summer but also had multiple sub-environments sampled (hummock, lawn and hollow, Salmisuo, Finland; bog and fen, Red Lake peatlands, Minnesota, USA). Porewater 13C(CH4) values at the majority of sites showed 13C-enrichment at shallow depth and progressively more negative 13C(CH4) values at greater depth. Such a trend is typically caused by a combination of methanotrophy near the water-air interface and preferential production of CH4 by acetate fermentation in the rhizosphere. The notable exception to the trend was the bog site in the Red Lake peatlands, Minnesota, USA, which had the most negative 13C(CH4) values in shallow layer typical of nutrient-poor acidic mires. The shallow depth of sampling (0 to 30 cm) at wetlands other than Salmisuo, Finland (0 to 200 cm) and Red Lake peatlands, Minnesota, USA (0 to 300 cm) limits to extent to which trends in 13C(CH4) values with depth can be examined. Peat in the depth range 0 to 30 cm commonly experiences seasonal variations in water table levels, which promotes CH4 oxidation. 72 Table 8. 13C values of porewater CH4 from different depths in northern peatlands reported since 2009. Depth 13C(CH4) SD n 13C(CH4) SD n 13C(CH4) SD n (cm) (‰) (‰) (‰) Komi Republic, Russia (Marushchak et al., 2016) 2007 2008 Seida Village Salix spp. 5 -59.3 8.1 4 -52.3 2 30 -62.8 2.5 5 -62.4 2 Carex spp. 5 -53.6 4.1 4 -52.3 2 30 -59.5 2.2 5 -62.4 2 Eriophorum spp. 5 -49.9 6.9 5 -53.9 2 30 -61.9 5.4 5 -53.9 2 Eastern Finland (Dorodnikov et al., 2013) Hummock Lawn Hollow Salmisuo (pine fen) 50 -63.0 1 -62.8 1 -63.5 1 100 -68.0 1 -68.4 1 -69.3 1 150 -68.7 1 -70.8 1 -70.0 1 200 -68.3 1 -70.0 1 -69.2 1 Minnesota, USA (Corbett et al., 2013) Bog Fen Red Lake peatlands 0 -71.3 1 -56.5 1 50 -67.9 1 -55.9 1 100 -66.2 1 -60.8 1 150 -65.7 1 -63.9 1 200 -65.2 1 -66.1 1 250 -65.0 1 -66.3 1 300 -64.1 1 -67.1 1 Sakha, Russia (Shingubara et al., 2019) 2011 2012 2013 Verkhny Khatistakha (larch forest bog) 0 -58.1 0.2 3 -56.0 2 -60.0 2 Larix gmelinii, Carex spp., E.angustifolium 10 -59.0 2 -51.0 1 -46.0 1 20 -53.0 1 -51.0 1 Kodac (tiga-tundra boundary) 0 Larix gmelinii, Sphagnum squarrosum 10 -56.0 9.0 6 -48.0 1 20 -55.0 10.0 3 -47 2.0 3 30 -49.0 2 -50 1 Kodac (tiga-tundra boundary) 0 -46.0 4.0 4 -51.0 9.0 10 -45.0 3.0 6 Larix gmelinii, Eriophorum angustifolium 10 -50.0 1.0 6 -52.0 3.0 7 -49.0 4.0 5 20 - -46.0 1.0 3 -54.0 10.0 5 30 - -50.0 2 -49.0 2.0 3 Boydom (low-centered polygon bog) 0 -40.0 2 -56.0 1 -48.0 2 Larix gmelinii, Eriophorum angustifolium 10 -47.0 2 -53.0 1 -51.0 1 20 -54.0 2 -53.0 1 30 -53.0 73 Table 9. 13C values of wetland CH4 emissions sampled from diel inversions, flux chambers and aircraft reported since 2009 Wetland site Sampling Dates 13C-CH4 (‰) Diel inversion and chamber samples Sodankylä, Finlanda 17–18 Jul 2012 -69.2 ± 0.6 21–22 Jul 2012 -71.2 ± 1.6 24–25 Jul 2012 -72.1 ± 2.0 17–19 Aug 2013 -72.5 ± 1.0 7–8 Sep 2012 -74.8 ± 0.9 3–4 Oct 2013 -78.1 ± 0.8 Lompolojänkkä, Finland 18–19 Jul 2012 -72.3 ± 3.2 7–8 Sep 2012 -68.0 ± 0.6 Kaamanen, Finland 22–23 Jul 2012 -71.7 ± 0.4 15–16 Aug 2013 -68.0 ± 1.2 9–10 Sep 2012 -74.0 ± 2.1 Faerdesmyra, Finland 23–24 Jul 2012 -72.0 ± 1.1 Stordalen Mire, Sweden Open mireb 15–16 Aug 2013 -68.6 ± 0.1 Tall graminoidsb 19–20 Aug 2013 -59.6 ± 0.7 Tall graminoidsb 1–2 Jul 2014 -63.2 ± 0.6 Open mireb 1–2 Jul 2014 -69.8 ± 1.8 Sphagnumc Aug 2013 -77.9 ± 1.6 Sphagnumc Sep 2013 -82.8 ± 2.1 Eriophorum angustifoliumd Aug 2013 -56.8 ± 0.5 Eriophorum angustifoliumd Sep 2013 -55.3 ± 1.0 Subarctic tundra, Russiane July – Aug 2007 -68.4 ± 2.0 Salmisuo, Finlandf Lawn (fen) 1-20 July 2009 -68 Hollow (fen) 1-20 July 2009 -69 Aircraft-based Northern Fennoscandiag 22 Jul 2012 -72.2 ± 3.3 15 Aug 2013 -64.1 ± 1.9 16 Aug 2013 -72.2 ± 1.2 16 Aug 2013 -71.3 ± 1.5 17 Aug 2013 -71.0 ± 0.6 17 Aug 2013 -70.3 ± 1.4 18 Aug 2013 -71.8 ± 2.1 20 Sep 2013 -71.1 ± 4.5 a The six 13C-CH4 values are diel inversion measurements. 13C-CH4 values measured using chambers ranged from -76 to -53‰, with high flux rates (> 5 mg CH4 m-2 h-1) having stable carbon isotope values ranging from 70 to -66‰. Emission weighted flux from chambers was -68.0 ± 3.4‰ (Fisher et al., 2017). b The 13C-CH4 values are diel inversion measurements in wetland types specified. Estimated emission-weighted 13C value for the entire wetland was -69.0‰ (Fisher et al., 2017). c Emission weighted 13C-CH4 values from flux chambers over Drained – hummock and shrub, Semiwet – Sphagnum dominated and Wet – Sphagnum dominated (Fisher et al., 2017) d Emission weighted 13C-CH4 values from Wet – tall graminoid (Eriophorum dominated) (Fisher et al., 2017). e Integrated 13C-CH4 signature from mixed landscape containing Salix, Carex and Eriophorum dominated wetlands (Marushchak et al., 2016). f The 13C-CH4 values were collected using flux chambers (Dorodnikov et al., 2013). g Data reported by Fisher et al. (2017). 74 Flux data collected using ground chambers and sampling of air during nocturnal inversions were reported from sites 1, 2, 9, 10, 11, 12. Table 9 also summarizes results from aircraft campaigns that sampled air at different elevations above peatland-rich areas in northern Fennoscandia. The 13C values of CH4 emissions from all three approaches (chambers, nighttime inversions and aircraft) were determined using regression analysis of 13C values vs. [CH4]-1 (ppmv-1) (i.e., a Keeling plot) from samples in which the concentration of CH4 was increasing with time due to emissions from wetlands (Fisher et al., 2017). The 13C values of CH4 flux reported from chamber and inversion measurements in different countries range from -78.1 to -68.0‰ for Finland, -82.8 to -55.3‰ for Sweden, and -68.4‰ (one site) for Russia. The 13C values for CH4 emissions detected above wetland-rich areas in northern Fennoscandia range from -72.2 to -64.1‰. 3.5 Discussion Loss of CH4 predominant from the shallow active zone in fens and bogs provides a means to trace CH4 emissions from these two wetland types because of the differences in methanogenic pathways that yield distinctive 13C values. The last compilation and assessment of stable carbon isotope data for wetland CH4 reported 13C values of -74.9 ± 9.8‰ (n = 42) for bogs and -64.8 ± 4.0‰ (n = 38) for fens (Hornibrook, 2009). The amount of new data published during the past decade is not substantially (Tables 8 and 9) but will be evaluated in the context of previous assessments of 13C(CH4) values for peatlands. 75 3.5.1 Porewater CH4 The shallow depths of sampling at sites 1 and 14a-c typically will not capture changes in 13C(CH4) values associated with transitions in methanogenic pathways, which occur in fens below the depth of the rhizosphere. Changes with depth in 13C(CH4) values at Seida Village, Russia (site 1) and Sakha, Russia (sites 14a-c) reflect a small and gradual decrease in 13C content in CH4, which is common in shallow porewater. Methane in soil near the ground surface and the water table generally exhibits a higher 13C content due to bacterial oxidation of CH4 which preferentially removes 12CH4 leaving behind an isotopically heavier residual. Deeper sampling of porewater at site 2 (Salmisuo, Eastern Finland) to 200 cm shows a distinctive decrease in 13C(CH4) values. The shallowest depth sampled (50 cm) is below the level at which methanotrophy typically occurs in porewater because it lies below the seasonal level of water table variation. Notably, CH4 at 50 cm depth in all three sub-environments (hummock, lawn, hollow) has 13C values (-63.0, -62.8, -63.5‰, respectively) that are more 13C-depleted than CH4 in porewater above 30 cm depth at the Seida Village (site 1) and Sakha (sites 14a-c) wetlands, reflecting unaltered CH4 that exists below the zone in which the water table and oxygen-levels usually fluctuate. A small further decrease in 13C content (i.e., more negative 13C values) occurs from 100 to 200 cm depth at Salmisuo fen, which is expected in the subsurface of minerotrophic wetlands due to a transition from acetate fermentation in shallow peat to CO2/H2 methanogenesis in deeper recalcitrant peat layers (Hornibrook et al., 2000). The same trend is even more evident in the fen site at the Red Lake peatland in Minnesota, USA where 13C(CH4) values range from -56.5 to -67.1‰ from 0 to 300 cm depth. In contrast, the bog site at the Red Lake peatland exhibits a trend from 13C-poor CH4 (-71.3‰) at 0 cm to greater 13C-enrichment (-64.1‰) at 300 cm depth, 76 which is characteristic of acid nutrient-poor wetlands where methanogenesis is dominated by CO2/H2 at all depths. The 13C-enrichment in CH4 with depth typically mirrors a similar trend in the 13C value of porewater dissolved inorganic carbon (DIC) because 13C(CH4) and 13C(DIC) values are coupled through the CO2/H2 methanogenic pathway. The depth trends of 13C(CH4) values in the fen and bog at Red Lake peatlands are typical of the patterns caused by differences in subsurface methanogenic pathways that result in distinctive 13C values of CH4 emissions to the atmosphere (Hornibrook and Bowes, 2007; Hornibrook, 2009). Preferential release of CH4 produced in shallow peat results in emission of CH4 possessing 13C values that reflect the methanogenic pathway differences that exist in bogs and fens. Methane emitted from bogs tends to be more 13C-poor (i.e., more negative 13C values) because the CH4 is produced from CO2/H2 methanogenesis at shallow depths where 13C(DIC) values also are more negative (typically ~-25‰) as a result of respiration or decomposition of C3 vegetation (13C ~-27‰). In contrast, an abundance of labile substrate at shallow depths in fens due to the present of graminoids leads to production of CH4 by acetate fermentation, which produce more 13C-enriched (i.e., more positive 13C values) and consequently, emission of CH4 similarly 13C-enriched relation to CH4 flux from bogs. Although the concentration of CH4 in shallow peat is less than within deeper peat horizons, the amount of CH4 at a particular depth reflects the net effects of CH4 production, oxidation and transport (Roulet et al., 1992). Production rates of CH4 are highest in shallow peat because of higher temperatures and the supply of labile substrates from root exudates (McKenzie et al., 1998; Smemo & Yavitt, 2006). The proximity to the water-air interface also results in a lower confining pressure and a shorter turnover rate of CH4 in shallow peat (e.g., Bowes and Hornibrook, 2006). 77 3.5.2 CH4 Emissions The two sites that reported both subsurface and emission data, showed good agreement between CH4 in porewater and emissions: Seida Village, Russia, range -62.8 to -53.9‰ at 30 cm depth (Table 8) vs. -68.4‰ (integrated landscape 13C(CH4) value; Table 9); Salmisuo, Finland, porewater range -70.8 to -62.8 ‰ (50 to 200 cm; Table 8) vs. flux -69 to -68‰ (Table 9). The porewater data at Seida Village extend to only 30 cm depth and it is unlikely that production of all CH4 emitted to the atmosphere occurred entirely within that depth interval. Notably the majority of 13C(CH4) value pairs from 5 and 30 cm samples showed a decreasing trend with depth and supply of CH4 emissions from depths >30 cm could account for the lower 13C content of CH4 emissions (i.e., -68.4‰) (Marushchak et al., 2016). Alternately, transport of CH4 through plant aerenchyma may have contributed to the lower 13C content in CH4 emissions as result of secondary fractionation that occurs during diffusion through plant stems (Chanton and Dacey, 1991; Chanton, 2005). A similar effect could account for the ~6 to 8‰ difference in 13C values between CH4 emissions (-69 to -68‰; Table 9) and porewater CH4 at 50 cm depth (-63.5 to -62.8‰; Table 8) at Salmisuo, Finland; however, CH4 in porewater at >50 cm depth has 13C values (-70.8 to 68.0‰; Table 8) that agree well with the 13C signature of CH4 emissions (Dorodnikov et al., 2013). The data do not permit a conclusive statement about the relative role of depth of CH4 production or secondary transport kinetic isotope effects through plants on the 13C values of CH4 emissions. This question similarly could not be addressed from data compiled by Hornibrook (2009) although there was a consistent ~12‰ 13C-depletion in CH4 emissions relative to the average 13C value of CH4 in porewater from 0 to 50 cm depth in that data set. 78 The remaining 13C(CH4) emission data in Table 9 for wetlands in Finland and Sweden were reported in an original study by Fisher et al. (2017). The results confirm two key observations from earlier literature: (i) CH4 emitted from minerotrophic graminoid-rich peatlands is 13C-enriched (i.e., has more positive 13C values) relative to CH4 flux from nutrient-poor acidic Sphaghum-dominated mires, and (ii) the integrated 13C value of CH4 emissions from boreal wetlands is substantially more negative than values used to date in isotope-weighted CH4 budgets and models. Flux measurements conducted in sub-environments at Stordalen Mire, Sweden best illustrate point (i). Methane emissions from the tall graminoid and Eriophorum angustifolim areas have 13C values (-63.2 ± 0.6‰ to -55.3 ± 1.0‰) that are distinctly more positive than CH4 flux from areas of acidic mire and Sphagnum-dominated areas (-82.8 ± 2.1‰ to -68.6 ± 0.1‰). The difference in 13C content of CH4 emitted could be due to a great prevalence of acetate fermentation at shallow depths in the graminoid-dominated areas or it may be the result of 13C-enrichment due to enhanced methanotrophy in the rhizosphere of the graminoids. Regardless of the cause, the study by Fisher et al. (2017) confirms that the 13C values of CH4 emitted from minerotrophic peatlands (as indicated by plant species composition) is more positive than CH4 flux from acidic, bryophyte-dominated mires. Flux measurements reported by Fisher et al. (2017) for sites in Finland collected using flux chambers and diel inversions do not make a distinction between minerotrophic and ombrotrophic peatlands. The landscapes are patterned, containing a mixture of both types of wetland, which results in blended or mixed 13C values from the two classes of wetlands. The areas are largely dominated though by Sphagnum-rich wetland (Fisher et al., 2017), which accounts for the 79 generally more negative 13C values of CH4 flux (i.e., -78.1 ± 0.8‰ to -68.0 ± 1.2‰; Table 9). These 13C values measured at ground-level agree well with aircraft-based measurements conducted over broader areas of northern Fennoscandia, which ranged from -72.2 ± 3.3‰ to -64.1 ± 1.9‰. Notably the more positive end of the range (-64.1 ± 1.9‰) was the only value measured from eight flight campaigns, which was more positive than -70‰ (Table 9). The observed 13C values are consistent with acidic ombrotrophic peatlands being the most common type of northern wetland and responsible for ~60% of total CH4 emissions from boreal wetlands (Matthews and Fung, 1987). The data presented in Table 9 re-enforce the need to better discern areas of bog and fen when attempting to estimate net 13C values of CH4 from boreal wetlands or use stable isotope to evaluate changes in boreal CH4 emission sources. As noted by Fisher et al. (2017), the average 13C value used to represent wetland CH4 emission in global budgets is much more 13C-enriched (e.g., -59‰, Monteil et al., 2011; -58‰, Bousquet et al., 2006). The authors note further that adopting a more negative 13C value for boreal wetlands typically provides better model agreement with observations of atmospheric CH4 13C values at high latitudes (Warwick et al., 2016). 3.5.3 Assigning 13C Values to Wetland CH4 Emissions While the data presented in Table 8 and Table 9 agree well with previous reports about the differences in 13C(CH4) values between fens and bogs, the compilation does not provide a strong basis for further refining 13C values attributed to CH4 emissions from bogs and fens. Only the emission data from Stordalen, Sweden (Fisher et al., 2017) and Salmisuo, Finland (Dorodnikov et al., 2013) are specific to bog and fen environments. Data for all other sites represents integrated fluxes from patterned landscapes in which the relative proportions of bog and fen were not defined. 80 Nonetheless, it is useful to note that the observed ranges of 13C values for the different northern regions (Table 9) agree well with the 13C(CH4) values determined previously for ombrotrophic (-74.9 ± 9.8‰) and minerotrophic (-64.8 ± 4.0‰) wetlands (Hornibrook, 2009). The 13C values from Hornibrook (2009) were used by Ganeson et al. (2017) to develop a global gridded 13C(CH4) wetland source signature map. Creation of the global map was challenging in part because of limited spatial data delineating bogs and fens, which required assumptions to be made about wetland coverage based upon soil pH data. In Chapter 4, the 13C values for ombrotrophic (-74.9 ± 9.8‰) and minerotrophic (-64.8 ± 4.0‰) wetlands will continue to be used for consistency in order to evaluate the integrated 13C value of CH4 flux from wetlands in Manitoba against global gridded data reported in Ganesan et al. (2017). The approach will better enable assessment of improvements provided by refining areal representation of wetland types using the remote sensing analysis approach developed in Chapter 2. 81 Chapter 4: Upscaling of CH4 flux and 13C values from wetlands in Manitoba 4.1 Synopsis In this chapter the inventory of wetlands determined for Manitoba, Canada (Chapter 2) was used to upscale 13C(CH4) values (Chapter 3) weighted using published CH4 flux rates for bogs and fens. Manitoban bogs and fens are estimated to emit 0.8 ± 0.1 Mt CH4 yr-1 collectively, which is a large proportion of total flux from northern Canadian wetlands (4.1 - 6.9 Mt). The flux-weighted 13C(CH4) values are more negative than compositions (-62 to -58‰) commonly attributed to wetlands in published literature because of the endmember compositions assigned to bogs and fens. The integrated values determined for Manitoba are consistent with 13C(CH4) values reported from diel inversion and aircraft studies. 4.2 Background Amongst the anthropogenic and natural emission sources of CH4, wetlands account for approximately 40% of global flux to the atmosphere (Cao et al., 1998; Petrescu et al., 2010); however, this important source is poorly characterized in the majority of ecological zones (Bridgham et al., 2013) in part due to the variety of local conditions (e.g., vegetation structure, hydrology regime, climate factors, etc.) that exist within commonly heterogeneous wetland landscapes (Matthews, 1993). While it is challenging to estimate CH4 emissions consistently from wetlands because of seasonal and annual variability in source strength, the stable isotope composition of CH4 emissions from different wetland classes has shown greater consistency and 82 has been employed for tracing methane emissions in global mass balance budgets (Whiticar et al., 1986, 1999; Bogner et al., 1995, 1996, 2000; Schaefer et al., 2016). A related issue is the stability of significant carbon stores in northern peatlands that could be converted to CO2 and CH4 in a warmer climate (Tarnocai et al., 2009). Northern wetlands globally are estimated to contain between ~ 400 and 600 Gt carbon stored as peat (Yu, 2012). Large areas of these peatlands also contain continuous or discontinuous permafrost melt, which is at risk of melting and creating new areas of peatland with active layers that generate CH4 (Baird et al., 2009; Tarnocai et al., 2009). The area occupied by wetlands and their prevalence at high northern latitudes makes CH4 emissions from these ecosystems one of the largest uncertainties in the global CH4 budget (Saunois, et al., 2016). The lack of accurate estimates of wetland area contributes further to the uncertainty in quantifying current and future emissions of CH4 from wetlands (Melton et al., 2013; Turetsky et al., 2014). However, considerable interest in measuring CH4 emissions from northern wetlands in recent decades (e.g., Budishchev et al., 2014; Davidson et al., 2019; Ito, 2019; Jeffrey et al., 2019; Peltola et al., 2019; Poulter et al., 2017; Saunois, Bousquet, et al., 2016; Wang et al., 2019; Yu, 2012) provides substantial data for efforts to quantify CH4 fluxes as progress continues to be made in refining estimates of wetland area. More accurate upscaling of CH4 flux from northern wetlands will contribute to better characterization of the global CH4 budget (Zhang et al., 2012; Baird et al., 2009). Bogs and fens have different metabolic pathways of CH4 production because of environmental conditions, in particular pH, which act upon the anaerobic chain of decay comprised of archaea and bacteria (Chanton et al., 2005). Environmental conditions impact the substrates that are metabolized to CH4 at shallow depths leading to differences in bogs (CO2/H2) and fens (acetic 83 acid) (Whiticar, 1999). These differences in metabolic substrate-based methanogenic pathways in bogs and fens can be employed as constraints on CH4 sources in combination with isotope fractionation effects of CH4 sinks (Tyler et al., 1997; Stevens, 1993). The pathway impacts are best characterized for stable carbon isotope (Brownlow et al., 2017). The aims of this chapter are to: a) attribute emission strength and 13C values to CH4 flux from bogs and fens in Manitoba; b) upscale the isotope-weighted CH4 emissions from bogs and fens to determine an integrated 13C(CH4) composition for the boreal regions in Manitoba; c) assess the variability of 13C(CH4) values for different ecozones and ecodistricts in Manitoba; and d) compare the determined integrated 13C(CH4) values to measured 13C(CH4) values from boreal zone wetlands and global CH4 budgets in published literature. The analysis in this chapter utilizes the wetland inventory maps for Manitoba developed in Chapter 2 and the assessment of 13C(CH4) values for bogs and fens reviewed in Chapter 3. 4.3 Methods 4.3.1 Estimation of the area of wetland types Four ecozones in Manitoba were evaluated for spatial scaling of CH4 emissions and 13C(CH4) values: Boreal Shield, Boreal Plain, Taiga Shield, and Hudson Plain (Figure 2; Chapter 2). The Southern Arctic and Prairie ecozones were not included in the analysis due to their small size and poor availability of relevant CH4 flux data for the zones. Data scaling was conducted at the ecodistricts scale, which is a more localized classification of ecozones. Ecodistricts are characterized by a distinctive collection of geology, landforms and soil, vegetation, water, fauna 84 and land use (Ecological Stratification Working Group, 1996; Wulder & Nelson, 2003). There are 1,027 ecodistricts in Canada of which 83 are situated in Manitoba. ArcGIS Desktop (version 10.5.1) was used to determine the area (km2) of each wetland type (Table 10) based upon the wetland inventory maps (Figures 5 to 9) generated in Chapter 2. A more detailed list of ecodistricts is provided in Appendix A1. The total area (Atotal) of wetland in each ecozone is represented by: (Atotal) = Abog + Afen + Amarsh + Aswamp + Ashallow/open water Eq. 15 The percentages of bog and fen in total wetland areas of Taiga Shield, Boreal Shield, Boreal Plain and Hudson Plain, respectively, are 19% and 7%, 30% and 21%, 13% and 48%, 22% and 23%. Table 10. The area of wetland types from maps 5 to 9 in each ecozone of Manitoba. Ecozone Bog (km2) Fen (km2) Marsh (km2) Swamp (km2) Shallow/open water (km2) Total (km2) Taiga Shield 11,128 4,403 20 52 44,420 60,023 Boreal Shield 29,002 19,922 93 4,518 40,373 93,908 Boreal Plain 3,795 13,699 938 1,175 8,819 28,426 Hudson Plain 7,971 8,155 13 9 19,467 35,615 Total (km2) 51,894 46,178 1,064 5,754 113,079 217,971 4.3.2 Estimation of CH4 flux Methane emission data reported by Webster et al. (2018) for Canadian peatlands were used to upscale CH4 fluxes from ecozones and ecodistricts in Manitoba. The data consist of several years of CH4 fluxes from a range of peatland types, including bog, poor fen and rich-intermediate fen. A total of 62 bog and 95 fen sites were studied in seven Canadian ecozones including Atlantic 85 Maritime, Boreal Plain, Boreal Shield, Hudson Plains, Mixed-wood Plain, Taiga Plain and Taiga Shield. The number of growing days in each ecodistrict was estimated using the procedure employed by Webster et al. (2018), which involved determining the number of days during which the average daily temperature was greater or equal to 5°C for five consecutive days and nighttime temperature remained above -2°C. The average daily CH4 emission rate for bog and fen determined from data in Table 11 were multiplied by the number of growing days and the areas of bog and fen in each ecozone and ecodistrict (Eq. 16): fluxecozone/district (t CH4 yr-1) = fluxbog/fen (g CH4 m-2 d-1) × growing daysecozone/district (d yr-1) × areabog/fen (km2) × 106 m2 km-2 × 10-6 t g-1 Eq. 16 A summary of the calculated fluxes for bogs and fens in each ecodistrict and totals for each ecozone are provided in Appendix A (Table A1). The CH4 fluxes from bogs and fens within the different ecozones and ecodistricts was combined with the attribute table of the respective layers in ArcGIS to produce the emission maps shown in Figure 14 and Figure 15. 4.3.3 Isotope-weighting CH4 emissions and sensitivity analysis The 13C values of CH4 fluxes were calculated for bog and fen sites in each ecodistrict and ecozone of Manitoba using the equation:  =(𝑏𝐹𝑏𝐴𝑏)+(𝑓𝐹𝑓𝐴𝑓)(𝐹𝑏𝐴𝑏)+(𝐹𝑓𝐴𝑓) Eq. 17 86 Where ̅ is the calculated flux-weight 13C value in permil (‰); b and f are the 13C values (‰) assigned to CH4 emission from bogs and fens, respectively; Fb and Ff are the mean CH4 flux (g CH4 m2 season-1) for bogs and fens, respectively; Ab and Af are the respective areas (m2) of bog and fen. The 13C values assigned to bogs (-74.4‰) and fens (-64.8‰) were adopted from (Hornibrook, 2009) on the basis of the review and analysis of available data conducted in Chapter 3. Table 11. Compilation of CH4 flux data for bogs and fens in Manitoba (Webster et al., 2018). Wetland type Reference Location Ecozone Average CH4 flux (mg m-2 d-1) Bog Rouse et al. (1995) Churchill Hudson Plains 0.1 0.2 Bubier et al. (1995) Thompson Boreal Shield 96.9 55.4 86.8 Total Average 40.0 Fen Bubier et al. (1995) Thompson Boreal Shield 102.2 84. 5 120 19.1 Roulet et al. (1994) Churchill Hudson Plains 78.6 Rouse et al. (1995) Churchill Hudson Plains 42.1 22.2 62.0 92.4 51.5 133.2 Bubier et al. (1995, 2005) Thompson Boreal Shield 200.6 151.3 250.0 69.3 68.7 70.0 16.3 5.7 55.3 Average 81.9 87 * The authors reported daily and seasonal emission of methane in Manitoba. Source: Webster et al. (2018) Given the variability in CH4 flux rates for bogs and fens, an analysis was conducted to assess the sensitivity of integrated landscape 13C(CH4) values (lower limit, mean and upper limit) to changes or shifts in relative emission rates from these two classes of wetland that are dominant in the landscape of Manitoba. Ten different scenarios for daily flux rate of CH4 were assumed ranging from 10 to 100 mg CH4 m-2 d-1 for bogs (average = 39.9 mg m-2 d-1; Table 10) and 50 to 140 mg m-2 d-1 for fens (average = 82 mg m-2 d-1; Table 11). The range of emission rates were substituted sequentially for each wetland type into Eq. 17 while other parameters were held constant. Each time, one considered value (mean, lower and upper limit) of 13C(CH4) from Hornibrook 2009 was applied in the Eq. 17 in order to monitor the actual change of the direction of the relationship. 4.4 Results 4.4.1 Methane emission flux from bog and fen The estimated CH4 flux for each ecozone ranges from 4,050 to 157,999 (t yr -1) for bogs and 32,853 to 222,765 (t yr -1) for fens (Figure 14). The significant ranges for the total CH4 fluxes in each ecozone are primarily the result of differences in the area of wetland land cover (Table 10), which increases substantially from south to north. The higher estimated flux rates for fens also are due to higher daily CH4 emission rate (82 mg CH4 m-2 d-1) assigned to fens based upon data reported by Webster et al. (2018). Despite the higher emission rates per unit area for fens, bogs are predicted to be the dominant source of CH4 in the Taiga Shield zone because the area of land covered by bog (~19%) is more 88 than twice as large as fen area (~7%). Boreal Shield is the largest ecozone in Manitoba; however, fens (~21% land cover) are estimated to be a more significant source of CH4 emissions than bogs (~31% land cover). Similarly, fens are predicted to have higher total CH4 emissions in the Boreal Plain (13% bog and 48% fen; Table 10) and the Hudson Plain (22% bog and 23% fen; Table 10) because of their higher land cover area and higher assigned daily flux rates. The same procedure was applied to estimate wetland CH4 flux at the ecodistrict scale (Figure 15), which provided a more localized estimate of emissions. Total flux per ecodistrict had a range of 48 to 24,634 t CH4 yr-1 for bogs and 536 to 60,130 t CH4 yr-1 for fens. The large ranges in the estimated emissions per ecodistrict are due primarily to changes in wetland land cover from south to north. 4.4.2 Isotope-weighted CH4 emissions Integrated estimates of 13C values for total CH4 flux from wetlands in ecodistricts and ecozones are shown in Figure 16. The 13C(CH4) values range from -74.4 to -65‰ for ecodistricts and -70 to -67‰ in ecozones, reflecting the relative contribution of CH4 flux from the two dominant wetland types (bogs -74.4‰ and fens -64.8‰). Wetland emissions from southern Manitoba are predicted to have a more positive 13C value compared to central and northern regions because of the greater prevalence of fens versus bogs. 89 Figure 14. Average flux of CH4 from bogs (a) and fens (b) of different ecozones, Manitoba. a) b) 90 Figure 15. Average flux from bogs (a) and fen (b) sites of different ecodistricts, Manitoba, Canada a) b) 91 Figure 16. Wetland 13C(CH4) source signature map (‰) for ecozones (a) and ecodistricts (b) of Manitoba a) b) 92 4.4.3 Sensitivity of 13C values to CH4 flux rates Sensitivity analysis of 13C values that result from gradually increasing CH4 flux in bogs from 10 to 100 mg m-2 d-1 in the four ecozones of Manitoba are shown in Figure 17 and Figure 18. The lower (a), mean (b) and upper (c) 13C values were included in considered scenarios for CH4 flux from Hornibrook 2009. Increasing emission rates from bogs results in a gradual shift towards the endmember 13C value (lower limit: -95.3‰ and mean: -74.4‰) for bogs, while in the case of upper limits 13C value (-55.2‰) the changes are toward more positive values of 13C and is similar to fens. Although, the area of bogs relative to fens also impacts the total flux-weighted 13C value for each ecozone. The changes of mean 13C value in zone are: Taiga Shield -67 to -72‰; Boreal Shield -66 to -71‰; Boreal Plain -66 to -70‰; and Hudson Plain -65 to -67‰. In contrast, increasing emission rates from fens (Figure 18) shifts the integrated 13C value towards more 13C-enriched values because of the more positive fen endmember 13C composition (lower limit -72.6‰, mean -64.8‰ and upper limit -55.6‰). The changes in each ecozone for the mean 13C value are: Taiga Shield -72 to -69‰; Boreal Shield -70 to -68‰; Boreal Plain -69 to -67‰; and Hudson Plain -69 to -67‰. 93 94 Figure 17. Change in 13C values of CH4 flux from bog areas in the four main ecozones of Manitoba as a function of ten different scenarios for CH4 flux (horizontal axis) as well as (a) lower (-95.3 ‰), (b) mean (-74.9 ‰) and (c) upper (-55.2 ‰) limits of 13C (‰) from Hornibrook 2009. Each scenario is parameterized using Eq. 17, which is based upon areal extent (m2), CH4 flux (g m-2 season-1) and three values for 13C (‰) of bog emissions in each ecozone. In the bog scenarios, the CH4 flux for fens is held constant at 82 mg m-2 d-1 while the CH4 emissions from bogs is changed stepwise from 10 to 100 mg m-2 d-1. For example, in scenario 3 with mean value of 13C in the Taiga Shield the 13C(CH4) (‰) was calculated as: [(areal extent of bog in ecozone × 30 mg m-2 d-1 CH4 flux × (-74.9‰)) + (areal extent of fen in ecozone × 82 mg m-2 d-1 CH4 flux) × (-68.4‰)] / [(areal extent of bog in ecozone × 30 mg m-2 d-1 CH4 flux) + (areal extent of fen in ecozone × 82 mg m-2 d-1 CH4 flux)]. Same method was used for the lower and upper limits of 13C (‰) for bog areas. The average number of growing days for each ecozone was used in each calculation. 95 96 Figure 18. Change in 13C values of CH4 flux from fen areas in the four main ecozones of Manitoba as a function of ten different scenarios for CH4 flux (horizontal axis) as well as (a) lower (-72.6 ‰), (b) mean (-64.8 ‰) and (c) upper (-55.6 ‰) limits of 13C (‰) from Hornibrook (2009). Each scenario is parameterized using Eq. 17, which is based upon areal extent (m2), CH4 flux (g m-2 season-1) and 13C (‰) of fen and bog emissions in each ecozone. In the fen scenarios, CH4 flux from bogs is held constant at 40 mg m-2 d-2 and the CH4 flux strength from fens changed stepwise from 50 to 140 mg m-2 d-1. For example, in fen scenario 3 with mean value of 13C in the Taiga Shield the 13C(CH4) (‰) was calculated as: [(areal extent of fen in ecozone × 70 mg m-2 d-1 CH4 flux × (-64.8‰)) + (areal extent of bog in ecozone × 40 mg m-2 d-1 CH4 flux) × (-74.9‰)] / [(areal extent of fen in ecozone × 70 mg m-2 d-1 CH4 flux) + (areal extent of bog in ecozone × 40 mg m-2 d-1 CH4 flux)]. Same method was used for the lower and upper limits of 13C (‰) for fen areas. The average number of growing days for each ecozone was used in each calculation. 4.5 Discussion Webster et al. (2018) estimate annual CH4 flux from Canadian wetlands to be 4.1  1.5 Mt CH4 yr-1, which is within the range predicted by previous studies based upon Canadian peatland maps (e.g., 6.9  4.1 Mt CH4 yr-1; Tarnocai et al., 2011). Based upon average daily flux rates of 39.9 mg m-2 d-1 and 82 mg m-2 d-1 for bogs and fens, respectively, and areas for each wetland class determined in Chapter 2, the current study estimates release of 0.8 ± 0.1 Mt CH4 yr-1 from these two classes of wetland situated solely in Manitoba. The proportion of total wetland CH4 flux emitted from the Manitoba landscape is substantial on a national scale because of the significant wetland cover in the boreal ecozones. At a field site scale, this study estimates CH4 emissions to be ~8 g C m-2 yr-1 on average for bogs and fens in Manitoba. The flux is expressed in units of g C rather than g CH4 to enable comparison with the annual average values of 7.6 to 15.7 g C m-2 yr-1 reported for northern peatlands globally (latitude 40° to 70°N) by Abdalla et al. (2016). The study by Abdalla et al. (2016) emphasized fens as a dominant CH4 emission source relative to bogs as a result of higher flux rates per unit area in fens, which is consistent with estimates in the present study. 97 Although CH4 emissions per unit area are lower in bogs than fen, the more negative 13C value associated with CH4 flux from bogs (Whiting & Chanton, 1993; Hornibrook, 2009) shifts the landscape integrated 13C(CH4) values to compositions (-74 to -65‰) that are substantially lower than 13C signatures typically assigned to wetland CH4 flux in global mass balance models (e.g.,–60‰ Whiticar, 1993; -59‰ Monteil et al., 2011; -58‰ Bousquet et al., 2006). The landscape integrated 13C(CH4) values become more negative in northern Manitoba due to greater bog cover (Camill, 1999; Halsey et al., 1997). The general pattern of integrated 13C(CH4) values determined in this study agrees well with Manitoba data from the globally-gridded 13C source signature map reported by Ganesan et al. (2018). The range of 13C values from this study (-74 to -65‰) is consistent with 13C values (-74 to -63‰) reported by Ganesan et al. (2018) for Manitoba. The upscaling assessment in this study, based upon inventories of bogs and fens in Manitoba (Chapter 2) and endmember 13C values associated with bog and fen CH4 emissions (Hornibrook, 2009), yielded landscape integrated 13C values (-74 to -65‰) that are similar to field measurements conducted in northern wetlands. As reviewed in Chapter 3, chamber and inversion studies reported 13C values of CH4 flux ranging from -78.1 to -68.0‰ for Finland (Fisher et al., 2017), -82.8 to -55.3‰ for Sweden (Fisher et al., 2017), and -68.4‰ for one site in Russia (Marushchak et al., 2016). The 13C values estimated in this study also overlap well with higher altitude measurements of CH4 in air collected by aircraft in northern Fennoscandia, which suggested that wetland emissions had 13C values ranging from -72.2 ± 3.3‰ to -64.1 ± 1.9‰ (Fisher et al., 2017). This study focused on CH4 emissions from bogs and fens because of their prevalence in the boreal wetland landscape. While the estimated 13C(CH4) values agree well with other reports in 98 the literature there is still scope for refining the upscaling assessment because of a present lack of 13C(CH4) data for swamps, marshes and open water areas. The areas of swamps and marshes are small relative to bogs and fens, but open water covers a significant part of the boreal zone landscape (Table 10). It is likely that CH4 emitted from shallow ponds and littoral zones of lakes experiences partial oxidation, which causes 13C-enrichment in the residual CH4 that is emitted to the atmosphere. A blend of such CH4 with bog and fen emissions could result in a shift to more positive 13C(CH4) values depending on the strength of shallow water CH4 source. An additional data type that would further constrain flux estimates is 2H(CH4) values. Whiticar et al. (1986) and Whiticar (1993) have suggested that methanogenesis by acetate fermentation and CO2/H2 methanogenesis generate CH4 with distinctive 2H values; however, to date there have been no reported directly measured 2H values for CH4 emissions from bogs or fens. Additionally, it is likely that a gradient in 2H values of CH4 flux exists from south to north as a result of changes in meteorite water 2H values, which influence 2H(CH4) values (Waldron et al. 1999). Combining 2H(CH4) and 13C(CH4) values should provide a stronger constraint on isotopic identification of sources and weighting in mass balance models. 99 Chapter 5: Conclusions and future directions 5.1 Conclusions The overarching goal of this study was to improve characterization of 13C values of CH4 emissions from boreal wetlands. A key contribution to achieving that goal was the development of an analytical method for creating probabilistic inventories of wetlands based upon freely available remote sensing data. A high spatial resolution comprehensive inventory of wetlands in Manitoba was developed based upon optical imagery, synthetic-aperture radar, pH, LiDAR-derived metrics, and topographic indices. Five wetland classes including bog, fen, swamp, marsh and open water were identified and mapped using the probabilistic random forest model. Indices derived from optical inputs had the greatest effect on model performance and discrimination of wetland types. Adding LiDAR-based metrics enhanced model performance for identifying and mapping wetland areas. Topographic indices had the least influence in the study due to the coarse resolution of data available for Manitoba. The wetland map developed for Manitoba was used primarily to quantify areas of bog and fen because of their important to overall CH4 emissions from boreal wetlands and the distinctiveness of the 13C values of their CH4 emissions. Other potential future uses include an assessment of carbon storage in boreal peatlands, which is another important global issue related to the impacts of climate change. The approach developed in this study also could be used for long-term monitoring of changes in the hydrology of boreal wetland ecosystems as a result of climate change and land use management. The second research objective was conducting a literature review to determine the most suitable CH4 emission strengths and 13C values to assign to CH4 emissions from boreal bogs and fens. 13C(CH4) data reported for northern wetlands since 2009 to 2020 were 100 compiled and organized by location and wetland type. The amount of new data was less than anticipated and reported measurements were primarily from sites in Russia and Fennoscandia. Use of diel sampling for collection of integrated 13C signatures for CH4 flux from patterned landscapes become more prevalent during the past decade although much of the data were collected and reported by one research group based in the UK. The new 13C(CH4) data from porewater in northern bogs and fens show subsurface trends similar to distributions reported previously, which appear to be controlled by methanogenic biochemical pathways. The differences in subsurface trends in 13C values lead to greater 13C enrichment in CH4 emissions from minerotrophic wetlands and more negative 13C values for CH4 flux from ombrotrophic bogs. In regions dominated by wetland complexes containing both bogs and fens, the 13C values of CH4 emissions at a landscape scale measured either through diel or aircraft sampling yield stable isotope compositions that are intermediate between 13C values associate with bog or fen CH4 emissions. Consequently, for consistency and to facilitate comparison with global gridded 13C(CH4) data reported by Ganesan et al. (2018), 13C values for bogs (-74.9 ± 9.8‰) and fens (-64.8 ± 4.0‰) used in upscaling calculations. There is a continued need for direct measurement data, and in particular, emission data collected together with subsurface 13C(CH4) data to advance mechanistic understanding of controls on the stable isotope values of CH4 emissions. In the data search it was also apparent that little progress has been made in collecting direct measurements of 2H(CH4) values from porewater or emission in wetlands, which has resulted in few advances in understanding of controls or patterns globally of the 2H values of wetland CH4 flux. 101 The third goal of the study was to determine integrated 13C compositions of CH4 flux for the ecodistricts and ecozones of boreal Manitoba. This objective was achieved by upscaling CH4 fluxes and 13C(CH4) values from bogs and fens in the province. The higher primary productivity of fens coupled with the abundance of graminoids that enable CH4 to bypass methanotropy and escape directly to the atmosphere results in higher CH4 emissions per unit area in fens than bogs. Predictably, fens had higher total CH4 emissions in ecodistricts and ecozones of Manitoba where their landscape cover was similar or greater than that of bogs. The estimated 13C(CH4) values agree with data reported previously in the literature from modelling studies (Ganesan et al., 2018) and direct integrated measurements of 13C(CH4) values based upon chamber, inversion and aircraft studies. The fourth goal of this research was assessing the possible range of 13C(CH4) values for the different ecozones and ecodistricts in Manitoba due to potential spatial or temporal variations in CH4 emission strength. This objective was addressed using a sensitivity analysis. Increasing emission rates from bogs while other factors were held constant resulted in a gradual shift towards the endmember 13C value for bogs. Conversely, increasing emission rates from fens shifted the integrated 13C value towards more 13C-enriched (i.e., fen endmember) values, which is due to the nature of the CH4 generation pathway in these two wetland types. Comparing the determinations of 13C(CH4) values for wetland emissions in Manitoba to 13C(CH4) values currently attributed to boreal zone wetlands in CH4 budgets was the last goal. This study estimates the collective annual average CH4 emissions from wetlands in Manitoba to be ~8 ± 1 g C m-2 yr-1 and the landscape integrated 13C values for wetland CH4 emissions to be in the range -74 to -65‰. Both outcomes are within ranges of values previously reported for boreal 102 wetland CH4 sources strength and stable isotope composition. The study has not resulted in changes to accepted values for these parameters but the model that has been developed has the potential now to improve national and international CH4 budgets by enabling better delineation of wetland classes and areas at a high resolution. The study has demonstrated that the wetland areas quantified using the approach yield integrated source CH4 strengths and isotope compositions that are within the broad uncertainty ranges currently attributed to wetland emissions in boreal landscapes. 5.2 Future directions The following topics would be useful avenues for future work on CH4 emissions from boreal wetlands that would extend the research reported in this study: • Test and apply the machine-learning probabilistic model to other areas in Canada and globally where field study data are available to refine calibration. • Use the model over time to assess changes in wetland classes and area relative to changes in isotope values of atmospheric CH4 being measured in the Canadian greenhouse gas network and AGAGE globally. • Initiate long-term studies of CH4 source strength and 13C and 2H values of CH4 flux to better inform upscaling at the ecodistrict scale. 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Ecodistrict Growing days Bog area (km2) Bog flux (t yr-1) Fen area (km2) Fen flux (t yr-1) 183 68.8 156 427 10 57 270 69.5 248 686 153 871 271 74.8 77 229 54 331 272 75.5 638 1922 460 2849 276 86.1 839 2881 598 4224 279 93.9 3415 12795 1367 10523 280 94.6 2009 7582 604 4687 281 90.4 2751 9921 401 2970 282 101.5 523 2117 315 2618 283 103 472 1938 441 3727 Taiga Sheild Ecozone 40500 32858 343 107.7 0 0 0 0 344 115.7 1108 5117 1177 11164 346 111.7 1019 4543 391 3584 349 119.4 572 2726 1088 10656 350 110.3 737 3244 702 6353 353 131.1 345 1806 2487 26734 355 113.5 408 1848 194 1804 356 127 419 2122 542 5640 357 134.3 334 1792 333 3670 358 157.6 2 10 39 510 359 143 589 3358 450 5281 360 116.8 4264 19871 1262 12089 361 118.4 170 805 313 3036 362 122 1343 6538 526 5262 363 133.3 789 4195 989 10812 364 141.5 3711 20952 2431 28207 365 131.4 1131 5928 1545 16647 366 145.5 2787 16182 345 4117 367 141.3 1224 6899 188 2181 368 146.9 1339 7851 588 7087 370 150.5 2464 14795 894 11036 371 160.9 3672 23574 2029 26765 373 165.4 502 3316 780 10577 375 188.1 10 73 319 4917 376 184.2 46 338 198 2987 377 181.8 16 113 105 1570 135 Ecodistrict Growing days Bog area (km2) Bog flux (t yr-1) Fen area (km2) Fen flux (t yr-1) 379 177.6 0 1 6 80 Boreal Shield Ecozone 157997 222765 663 141.8 1241 7019 361 4202 664 157.6 35 219 134 1730 665 144.7 383 2211 192 2281 666 150.2 179 1072 456 5614 667 151.2 134 810 246 3055 668 155.2 97 603 313 3983 669 156.9 1 5 168 2167 670 148.5 137 810 44 536 671 175 1 5 53 761 672 158.7 570 3611 349 4547 674 161.1 54 349 253 3339 675 159.9 22 139 225 2956 676 152.8 387 2358 813 10185 677 170.7 288 1964 222 3111 709 164.9 23 153 229 3097 714 140.1 7 40 182 2094 715 166.2 9 62 325 4434 716 163.3 9 61 832 11140 717 167.2 43 289 1706 23391 718 170.3 126 858 1100 15366 720 167.3 3 22 158 2165 723 165.2 9 59 4439 60130 724 175 36 250 897 12869 Boreal Plain Ecozone 73035 180987 751 178.4 1 7 262 3832 752 175.5 0 2 81 1165 753 172.4 31 213 493 6967 757 181.2 2 16 51 757 758 187.3 0 2 15 231 759 177.6 4 25 8 112 839 169.0 21 144 89 1232 840 173.4 7 48 90 1280 841 178.1 12 85 916 13384 843 177.6 4 26 156 2268 844 176.4 12 81 138 1999 846 178.9 1 9 390 5720 847 185.7 14 107 77 1170 848 186.0 3 23 24 364 849 190.2 5 35 94 1462 850 188.4 4 32 65 1011 851 190.7 8 60 52 811 1020 75.4 578 1738 276 1704 1022 83.1 339 1123 635 4327 136 Ecodistrict Growing days Bog area (km2) Bog flux (t yr-1) Fen area (km2) Fen flux (t yr-1) 1024 98.2 6287 24634 3339 26891 1025 97.2 425 1649 321 2558 1026 90.2 213 768 583 4309 Hudson Plain Ecozone 30829 83553
Thesis/Dissertation
eng
Earth and Environmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Refining estimates of the stable carbon isotope signature attributed to methane emissions from boreal wetlands
Text